Theory of cell fate

Emerging Role of Fibroblasts in Vitiligo: A Formerly Underestimated Rising Star

Vitiligo is a disfiguring depigmentation disorder characterized by loss of melanocytes. Although numerous studies have been conducted on the pathogenesis of vitiligo, the underlying mechanisms remain unclear. Although most studies have focused on melanocytes and keratinocytes, growing evidence suggests the involvement of dermal fibroblasts, residing deeper in the skin. This review aims to elucidate the role of fibroblasts in both the physiological regulation of skin pigmentation and their pathological contribution to depigmentation, with the goal of shedding light on the involvement of fibroblasts in vitiligo. The topics covered in this review include alterations in the secretome, premature senescence, autophagy dysfunction, abnormal extracellular matrix, autoimmunity, and metabolic changes.

Responses of organ precursors to correct and incorrect inductive signals

During embryonic development, the inductive molecules produced by local origins normally arrive at their target tissues in a nondirectional, diffusion manner. The target organ precursor cells must correctly interpret these inductive signals to ensure proper specification/differentiation, which is dependent on two prerequisites: (i) obtaining cell-intrinsic competence; and (ii) receiving correct inductive signals while resisting incorrect ones. Gain of intrinsic competence could avoid a large number of misinductions because the incompetent cells are nonresponsive to inductive signals. However, in cases of different precursor cells with similar competence and located in close proximity, resistance to incorrect inductive signals is essential for accurate determination of cell fate. Here we outline the mechanisms of how organ precursors respond to correct and incorrect inductive signals.

Tail and Spinal Cord Regeneration in Urodelean Amphibians

Urodelean amphibians can regenerate the tail and the spinal cord (SC) and maintain this ability throughout their life. This clearly distinguishes these animals from mammals. The phenomenon of tail and SC regeneration is based on the capability of cells involved in regeneration to dedifferentiate, enter the cell cycle, and change their (or return to the pre-existing) phenotype during de novo organ formation. The second critical aspect of the successful tail and SC regeneration is the mutual molecular regulation by tissues, of which the SC and the apical wound epidermis are the leaders. Molecular regulatory systems include signaling pathways components, inflammatory factors, ECM molecules, ROS, hormones, neurotransmitters, HSPs, transcriptional and epigenetic factors, etc. The control, carried out by regulatory networks on the feedback principle, recruits the mechanisms used in embryogenesis and accompanies all stages of organ regeneration, from the moment of damage to the completion of morphogenesis and patterning of all its structures. The late regeneration stages and the effects of external factors on them have been poorly studied. A new model for addressing this issue is herein proposed. The data summarized in the review contribute to understanding a wide range of fundamentally important issues in the regenerative biology of tissues and organs in vertebrates including humans.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Regulation of developmental gatekeeping and cell fate transition by the calpain protease DEK1 in Physcomitrium patens

Calpains are cysteine proteases that control cell fate transitions whose loss of function causes severe, pleiotropic phenotypes in eukaryotes. Although mainly considered as modulatory proteases, human calpain targets are directed to the N-end rule degradation pathway. Several such targets are transcription factors, hinting at a gene-regulatory role. Here, we analyze the gene-regulatory networks of the moss Physcomitrium patens and characterize the regulons that are misregulated in mutants of the calpain DEFECTIVE KERNEL1 (DEK1). Predicted cleavage patterns of the regulatory hierarchies in five DEK1-controlled subnetworks are consistent with a pleiotropic and regulatory role during cell fate transitions targeting multiple functions. Network structure suggests DEK1-gated sequential transitions between cell fates in 2D-to-3D development. Our method combines comprehensive phenotyping, transcriptomics and data science to dissect phenotypic traits, and our model explains the protease function as a switch gatekeeping cell fate transitions potentially also beyond plant development.

Contribution of Autophagy to Epithelial Mesenchymal Transition Induction during Cancer Progression

Epithelial Mesenchymal Transition (EMT) is a dedifferentiation process implicated in many physio-pathological conditions including tumor transformation. EMT is regulated by several extracellular mediators and under certain conditions it can be reversible. Autophagy is a conserved catabolic process in which intracellular components such as protein/DNA aggregates and abnormal organelles are degraded in specific lysosomes. In cancer, autophagy plays a controversial role, acting in different conditions as both a tumor suppressor and a tumor-promoting mechanism. Experimental evidence shows that deep interrelations exist between EMT and autophagy-related pathways. Although this interplay has already been analyzed in previous studies, understanding mechanisms and the translational implications of autophagy/EMT need further study. The role of autophagy in EMT is not limited to morphological changes, but activation of autophagy could be important to DNA repair/damage system, cell adhesion molecules, and cell proliferation and differentiation processes. Based on this, both autophagy and EMT and related pathways are now considered as targets for cancer therapy. In this review article, the contribution of autophagy to EMT and progression of cancer is discussed. This article also describes the multiple connections between EMT and autophagy and their implication in cancer treatment.

Cell Fate: What's Evolution Got to Do With It?

Theoretical frameworks concerning cell fate typically center on proximate causes to explain how cells know what type they are meant to become. While major advances in cell fate theory have been achieved by these mechanism-focused frameworks, there are some aspects of cell decision-making that require an evolutionary interpretation. While mechanistic biologists sometimes turn to evolutionary theory to gain insights about cell fate (cancer is a good example), it is not entirely clear in cell fate theory what insights evolutionary theory can add, and why in some cases it is required for understanding cell fate. In this perspective we draw on our work on cellular mortality to illustrate how evolutionary theory provides an explanation for death being selected as one of the potential cell fates. Using our hypothesis for why some microbes in a community choose death as their fate, we suggest that some insights in cell fate theory are inaccessible to a theoretical framework that focuses solely on proximate causes.

A Strainer-Based Platform for the Collection and Immunolabeling of Porcine Epidemic Diarrhea Virus-Infected Porcine Intestinal Organoid

The development of organoid research has raised new requirements for this methodology. In a previous study, we demonstrated that an emerging protocol achieved the collection, loading, and programmed immunolabeling of mouse intestinal organoids based on a strainer platform. To uncover the applied potential of this novel methodology on organoids from other species, the strainer platform was utilized to characterize the porcine epidemic diarrhea virus (PEDV)-infected porcine intestinal organoid model. Based on a previous study, some steps were changed to improve the efficiency of the assay by simplifying the reagent addition procedure. In addition, we redefined the range of strainer sizes on porcine intestinal organoids, showing that strainers with pore sizes of 40 and 70 μm matched the above protocol well. Notably, the strainer platform was successfully used to label viral proteins, laying the foundation for its application in the visualization of viral infection models. In summary, the potential of the strainer platform for organoid technology was explored further. More extensive exploration of this platform will contribute to the development of organoid technology.

Feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.

Cell state transitions: catch them if you can

The Company of Biologists' 2022 workshop on 'Cell State Transitions: Approaches, Experimental Systems and Models' brought together an international and interdisciplinary team of investigators spanning the fields of cell and developmental biology, stem cell biology, physics, mathematics and engineering to tackle the question of how cells precisely navigate between distinct identities and do so in a dynamic manner. This second edition of the workshop was organized after a successful virtual workshop on the same topic that took place in 2021.

Common immunopathogenesis of central nervous system diseases: the protein-homeostasis-system hypothesis

There are hundreds of central nervous system (CNS) diseases, but there are few diseases for which the etiology or pathogenesis is understood as well as those of other organ-specific diseases. Cells in the CNS are selectively protected from external and internal insults by the blood–brain barrier. Thus, the neuroimmune system, including microglia and immune proteins, might control external or internal insults that the adaptive immune system cannot control or mitigate. The pathologic findings differ by disease and show a state of inflammation that reflects the relationship between etiological or inflammation-inducing substances and corresponding immune reactions. Current immunological concepts about infectious diseases and infection-associated immune-mediated diseases, including those in the CNS, can only partly explain the pathophysiology of disease because they are based on the idea that host cell injury is caused by pathogens. Because every disease involves etiological or triggering substances for disease-onset, the protein-homeostasis-system (PHS) hypothesis proposes that the immune systems in the host control those substances according to the size and biochemical properties of the substances. In this article, I propose a common immunopathogenesis of CNS diseases, including prion diseases, Alzheimer’s disease, and genetic diseases, through the PHS hypothesis.

Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo

The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

Heterogeneity and 'memory' in stem cell populations

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories-changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli-are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

Theory of cell fate

Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell "fate", and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Fates Models of Systems Properties and Processes > Cellular Models.