Increased robustness of early embryogenesis through collective decision-making by key transcription factors

Correspondence between multiple signaling and developmental cellular patterns: a computational perspective

The spatial arrangement of variant phenotypes during stem cell division plays a crucial role in the self-organization of cell tissues. The patterns observed in these cellular assemblies, where multiple phenotypes vie for space and resources, are largely influenced by a mixture of different diffusible chemical signals. This complex process is carried out within a chronological framework of interplaying intracellular and intercellular events. This includes receiving external stimulants, whether secreted by other individuals or provided by the environment, interpreting these environmental signals, and incorporating the information to designate cell fate. Here, given two distinct signaling patterns generated by Turing systems, we investigated the spatial distribution of differentiating cells that use these signals as external cues for modifying the production rates. By proposing a computational map, we show that there is a correspondence between the multiple signaling and developmental cellular patterns. In other words, the model provides an appropriate prediction for the final structure of the differentiated cells in a multi-signal, multi-cell environment. Conversely, when a final snapshot of cellular patterns is given, our algorithm can partially identify the signaling patterns that influenced the formation of the cellular structure, provided that the governing dynamic of the signaling patterns is already known.

A computational model of stem cells' internal mechanism to recapitulate spatial patterning and maintain the self-organized pattern in the homeostasis state

The complex functioning of multi-cellular tissue development relies on proper cell production rates to replace dead or differentiated specialized cells. Stem cells are critical for tissue development and maintenance, as they produce specialized cells to meet the tissues' demands. In this study, we propose a computational model to investigate the stem cell's mechanism, which generates the appropriate proportion of specialized cells, and distributes them to their correct position to form and maintain the organized structure in the population through intercellular reactions. Our computational model focuses on early development, where the populations overall behavior is determined by stem cells and signaling molecules. The model does not include complicated factors such as movement of specialized cells or outside signaling sources. The results indicate that in our model, the stem cells can organize the population into a desired spatial pattern, which demonstrates their ability to self-organize as long as the corresponding leading signal is present. We also investigate the impact of stochasticity, which provides desired non-genetic diversity; however, it can also break the proper boundaries of the desired spatial pattern. We further examine the role of the death rate in maintaining the system's steady state. Overall, our study sheds light on the strategies employed by stem cells to organize specialized cells and maintain proper functionality. Our findings provide insight into the complex mechanisms involved in tissue development and maintenance, which could lead to new approaches in regenerative medicine and tissue engineering.

A computational model of stem cells' decision-making mechanism to maintain tissue homeostasis and organization in the presence of stochasticity

The maintenance of multi-cellular developed tissue depends on the proper cell production rate to replace the cells destroyed by the programmed process of cell death. The stem cell is the main source of producing cells in a developed normal tissue. It makes the stem cell the lead role in the scene of a fully formed developed tissue to fulfill its proper functionality. By focusing on the impact of stochasticity, here, we propose a computational model to reveal the internal mechanism of a stem cell, which generates the right proportion of different types of specialized cells, distribute them into their right position, and in the presence of intercellular reactions, maintain the organized structure in a homeostatic state. The result demonstrates that the spatial pattern could be harassed by the population geometries. Besides, it clearly shows that our model with progenitor cells able to recover the stem cell presence could retrieve the initial pattern appropriately in the case of injury. One of the fascinating outcomes of this project is demonstrating the contradictory roles of stochasticity. It breaks the proper boundaries of the initial spatial pattern in the population. While, on the flip side of the coin, it is the exact factor that provides the demanded non-genetic diversity in the tissue. The remarkable characteristic of the introduced model as the stem cells' internal mechanism is that it could control the overall behavior of the population without need for any external factors.

The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network

Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing significant roles in cell-fate commitments have been extensively studied, integrating such knowledge into the dynamic models to understand the underlying biological mechanism remains challenging. The hypothesis and modelling approach of the endogenous network has been developed previously and tested in various biological processes and is used in the present study of haematopoietic lineage commitments. The endogenous network is constructed based on the key transcription factors and their interactions that determine haematopoietic cell-fate decisions at each lineage branchpoint. We demonstrate that the process of haematopoietic lineage commitments can be reproduced from the landscape which orchestrates robust states of network dynamics and their transitions. Furthermore, some non-trivial characteristics are unveiled in the dynamical model. Our model also predicted previously under-represented regulatory interactions and heterogeneous MPP states by which distinct differentiation routes are intermediated. Moreover, network perturbations resulting in state transitions indicate the effects of ectopic gene expression on cellular reprogrammes. This study provides a predictive model to integrate experimental data and uncover the possible regulatory mechanism of haematopoietic lineage commitments.

A computational model of stem cell molecular mechanism to maintain tissue homeostasis

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, affects cell decision-making to maintain homeostatic state. The noise effect on tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

Noise-driven cell differentiation and the emergence of spatiotemporal patterns

The emergence of phenotypic diversity in a population of cells and their arrangement in space and time is one of the most fascinating features of living systems. In fact, understanding multicellularity is unthinkable without explaining the proximate and the ultimate causes of cell differentiation in time and space. Simpler forms of cell differentiation can be found in unicellular organisms, such as bacterial biofilm, where reversible cell differentiation results in phenotypically diverse populations. In this manuscript, we attempt to start with the simple case of reversible nongenetic phenotypic to construct a model of differentiation and pattern formation. Our model, which we refer to as noise-driven differentiation (NDD) model, is an attempt to consider the prevalence of noise in biological systems, alongside what is known about genetic switches and signaling, to create a simple model which generates spatiotemporal patterns from bottom-up. Our simulations indicate that the presence of noise in cells can lead to reversible differentiation and the addition of signaling can create spatiotemporal pattern.

Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas

The classical development hierarchy of pancreatic cell fate commitments describes that multipotent progenitors (MPs) first bifurcate into tip cells and trunk cells, and then these cells give rise to acinar cells and endocrine/ductal cells separately. However, lineage tracings reveal that pancreatic progenitors are highly heterogeneous in tip and trunk domains in embryonic pancreas. The progenitor fate commitments from multipotency to unipotency during early pancreas development is insufficiently characterized. In pursuing a mechanistic understanding of the complexity in progenitor fate commitments, we construct a core endogenous network for pancreatic lineage decisions based on genetic regulations and quantified its intrinsic dynamic properties using dynamic modeling. The dynamics reveal a developmental landscape with high complexity that has not been clarified. Not only well-characterized pancreatic cells are reproduced, but also previously unrecognized progenitors-tip progenitor (TiP), trunk progenitor (TrP), later endocrine progenitor (LEP), and acinar progenitors (AciP/AciP2) are predicted. Further analyses show that TrP and LEP mediate endocrine lineage maturation, while TiP, AciP, AciP2 and TrP mediate acinar and ductal lineage maturation. The predicted cell fate commitments are validated by analyzing single-cell RNA sequencing (scRNA-seq) data. Significantly, this is the first time that a redefined hierarchy with detailed early pancreatic progenitor fate commitment is obtained.

Development and utilization of human decidualization reporter cell line uncovers new modulators of female fertility

Failure of embryo implantation accounts for a significant percentage of female infertility. Exquisitely coordinated molecular programs govern the interaction between the competent blastocyst and the receptive uterus. Decidualization, the rapid proliferation and differentiation of endometrial stromal cells into decidual cells, is required for implantation. Decidualization defects can cause poor placentation, intrauterine growth restriction, and early parturition leading to preterm birth. Decidualization has not yet been systematically studied at the genetic level due to the lack of a suitable high-throughput screening tool. Herein we describe the generation of an immortalized human endometrial stromal cell line that uses yellow fluorescent protein under the control of the prolactin promoter as a quantifiable visual readout of the decidualization response (hESC-PRLY cells). Using this cell line, we performed a genome-wide siRNA library screen, as well as a screen of 910 small molecules, to identify more than 4,000 previously unrecognized genetic and chemical modulators of decidualization. Ontology analysis revealed several groups of decidualization modulators, including many previously unappreciated transcription factors, sensory receptors, growth factors, and kinases. Expression studies of hits revealed that the majority of decidualization modulators are acutely sensitive to ovarian hormone exposure. Gradient treatment of exogenous factors was used to identify EC₅₀ values of small-molecule hits, as well as verify several growth factor hits identified by the siRNA screen. The high-throughput decidualization reporter cell line and the findings described herein will aid in the development of patient-specific treatments for decidualization-based recurrent pregnancy loss, subfertility, and infertility.