Behavior Of Individual Cells Research Articles

Simulation of Somatic Evolution Through the Introduction of Random Mutation to the Rules of Conway’s Game of Life

Abstract Introduction Conway’s Game of Life (GOL), and related cellular automata (CA) models, have served as interesting simulations of complex behaviors resulting from simple rules of interactions between neighboring cells, that sometime resemble the growth and reproduction of living things. Thus, CA has been applied towards understanding the interaction and reproduction of single-cell organisms, and the growth of larger, disorganized tissues such as tumors. Surprisingly, however, there have been few attempts to adapt simple CA models to recreate the evolution of either new species, or subclones within a multicellular, tumor-like tissue. Methods In this article, I present a modified form of the classic Conway’s GOL simulation, in which the three integer thresholds that define GOL (number of neighboring cells, below which a cell will “die of loneliness”; number of neighboring cells, above which a cell will die of overcrowding; and number of neighboring cells that will result in spontaneous birth of a new cell within an empty lattice location) are occasionally altered with a randomized mutation of fractional magnitude during new “cell birth” events. Newly born cells “inherit” the current mutation state of a neighboring parent cell, and over the course of 10,000 generations these mutations tend to accumulate until they impact the behaviors of individual cells, causing them to transition from the sparse, small patterns of live cells characteristic of GOL into a more dense, unregulated growth resembling a connected tumor tissue. Results The mutation rate and mutation magnitude were systematically varied in repeated randomized simulation runs, and it was determined that the most important mutated rule for the transition to unregulated, tumor-like growth was the overcrowding threshold, with the spontaneous birth and loneliness thresholds being of secondary importance. Spatial maps of the different “subclones” of cells that spontaneously develop during a typical simulation trial reveal that cells with greater fitness will overgrow the lattice and proliferate while the less fit, “wildtype” GOL cells die out and are replaced with mutant cells. Conclusions This simple modeling approach can be easily modified to add complexity and more realistic biological details, and may yield new understanding of cancer and somatic evolution.

Inertial effect of cell state velocity on the quiescence-proliferation fate decision

Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that a combination of initial position and velocity on a p21/Cdk2 landscape, but not position alone, was required to explain the observed cell fate heterogeneity under hypoxia. This is likely due to additional cell state information such as epigenetic features and/or other species encoded in velocity but missing in instantaneous position determined by p21 and Cdk2 levels alone. Here, velocity dependence manifested as inertia: cells with higher cell cycle velocities prior to hypoxia continued progressing along the cell cycle under hypoxia, resisting the change in landscape towards cell cycle exit. Such inertial effects may markedly influence cell fate trajectories in tumors and other dynamically changing microenvironments where cell state transitions are governed by coordination across several biochemical species.

Profiling Dynamic Patterns of Single-Cell Motility.

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n = 74253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.

Cell fate simulation reveals cancer cell features in the tumor microenvironment

To elucidate the dynamic evolution of cancer cell characteristics within the tumor microenvironment (TME), we developed an integrative approach combining single-cell tracking, cell fate simulation, and three-dimensional (3D) TME modeling. We began our investigation by analyzing the spatiotemporal behavior of individual cancer cells in cultured pancreatic (MiaPaCa2) and cervical (HeLa) cancer cell lines, with a focus on the α2-6 sialic acid (α2-6Sia) modification on glycans, which is associated with cell stemness. Our findings revealed that MiaPaCa2 cells exhibited significantly higher levels of α2-6Sia modification, correlating with enhanced reproductive capabilities, whereas HeLa cells showed less prevalence of this modification. To accommodate the in vivo variability of α2-6Sia levels, we employed a cell fate simulation algorithm that digitally generates cell populations based on our observed data while varying the level of sialylation, thereby simulating cell growth patterns. Subsequently, we performed a 3D TME simulation with these deduced cell populations, considering the microenvironment that could impact cancer cell growth. Immune cell landscape information derived from 193 cervical and 172 pancreatic cancer cases was used to estimate the degree of the positive or negative impact. Our analysis suggests that the deduced cells generated based on the characteristics of MiaPaCa2 cells are less influenced by the immune cell landscape within the TME compared to those of HeLa cells, highlighting that the fate of cancer cells is shaped by both the surrounding immune landscape and the intrinsic characteristics of the cancer cells.

A Droplet-Based Microfluidic Platform for High-Throughput Culturing of Yeast Cells in Various Conditions.

Yeast plays a significant role in a variety of fields. In particular, it is extensively used as a model organism in genetics and cellular biology studies, and is employed in the production of vaccines, pharmaceuticals, and biofuels. Traditional "bulk"-based studies on yeast growth often overlook cellular variability, emphasizing the need for single-cell analysis. Micro-droplets, tiny liquid droplets with high surface-area-to-volume ratios, offer a promising platform for investigating single or a small number of cells, allowing precise control and monitoring of individual cell behaviors. Microfluidic devices, which facilitate the generation of micro-droplets, are advantageous due to their reduced volume requirements and ability to mimic in vivo micro-environments. This study introduces a custom-designed microfluidic device to encapsulate yeasts in micro-droplets under various conditions in a parallel manner. The results reveal that optimal glucose concentrations promoted yeast growth while cycloheximide and Cu2+ ions inhibited it. This platform enhances yeast cultivation strategies and holds potential for high-throughput single-cell investigations in more complex organisms.

CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications.

The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.

Microfluidics single-cell encapsulation reveals that poly-l-lysine-mediated stem cell adhesion to alginate microgels is crucial for cell-cell crosstalk and its self-renewal

Microfluidic cell encapsulation has provided a platform for studying the behavior of individual cells and has become a turning point in single-cell analysis during the last decade. The engineered microenvironment, along with protecting the immune response, has led to increasingly presenting the results of practical and pre-clinical studies with the goals of disease treatment, tissue engineering, intelligent control of stem cell differentiation, and regenerative medicine. However, the significance of cell-substrate interaction versus cell-cell communications in the microgel is still unclear. In this study, monodisperse alginate microgels were generated using a flow-focusing microfluidic device to determine how the cell microenvironment can control human bone marrow-derived mesenchymal stem cells (hBMSCs) viability, proliferation, and biomechanical features in single-cell droplets versus multi-cell droplets. Collected results show insufficient cell proliferation (234 % and 329 %) in both single- and multi-cell alginate microgels. Alginate hydrogels supplemented with poly-l-lysine (PLL) showed a better proliferation rate (514 % and 780 %) in a comparison of free alginate hydrogels. Cell stiffness data illustrate that hBMSCs cultured in alginate hydrogels have higher membrane flexibility and migration potency (Young's modulus equal to 1.06 kPa), whereas PLL introduces more binding sites for cell attachment and causes lower flexibility and migration potency (Young's modulus equal to 1.83 kPa). Considering that cell adhesion is the most important parameter in tissue engineering, in which cells do not run away from a 3D substrate, PLL enhances cell stiffness and guarantees cell attachments. In conclusion, cell attachment to PLL-mediated alginate hydrogels is crucial for cell viability and proliferation. It suggests that cell-cell signaling is good enough for stem cell viability, but cell-PLL attachment alongside cell-cell signaling is crucial for stem cell proliferation and self-renewal.

Decoding dynamic bamboo cell shrinkage with time-lapse microscopy and machine-learning

The shrinkage of bamboo can be traced to the cellular scale; however, research on the shrinkage of bamboo cells’ scale is rare. This gap primarily arises from the intrinsic heterogeneity of biomass materials, leading to significant differences in the shrinkage behaviors of bamboo cells. Consequently, conventional measurement and analysis techniques struggle to obtain comprehensive and accurate results. To fill in this gap, this study investigated the dynamic shrinking behavior of individual bamboo cells, including fiber cells and parenchyma cells, using time-lapse microscopy and machine-learning techniques. Time-lapse observations revealed that the shrinkage rate of fiber cells across the cross-section surpassed that of parenchyma cells, accompanied by a greater shrinkage deformation. Specifically, fiber cells exhibited a shrinkage rate of 2.53 %/min, whereas parenchyma cells showed a rate of 0.58 %/min. This resulted in corresponding diameter reductions of approximately −30 % and −7 %, respectively. Additionally, machine-learning results demonstrated the efficacy of the trained long short-term memory (LSTM) model in accurately predicting morphological changes in individual parenchyma cells during the shrinkage process. This study advances understanding of bamboo-moisture interaction mechanisms and offers crucial theoretical data for improving bamboo drying and minimizing crack formation.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms.

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Abstract SY45-02: Engineering strategies based on receptors derived from gdT cells

Abstract SY45-02: Engineering strategies based on receptors derived from gdT cells

A single point mutation in the Listeria monocytogenes ribosomal gene rpsU enables SigB activation independently of the stressosome and the anti-sigma factor antagonist RsbV.

Microbial population heterogeneity leads to different stress responses and growth behavior of individual cells in a population. Previously, a point mutation in the rpsU gene (rpsUG50C) encoding ribosomal protein S21 was identified in a Listeria monocytogenes LO28 variant, which leads to increased multi-stress resistance and a reduced maximum specific growth rate. However, the underlying mechanisms of these phenotypic changes remain unknown. In L. monocytogenes, the alternative sigma factor SigB regulates the general stress response, with its activation controlled by a series of Rsb proteins, including RsbR1 and anti-sigma factor RsbW and its antagonist RsbV. We combined a phenotype and proteomics approach to investigate the acid and heat stress resistance, growth rate, and SigB activation of L. monocytogenes EGDe wild type and the ΔsigB, ΔrsbV, and ΔrsbR1 mutant strains. While the introduction of rpsUG50C in the ΔsigB mutant did not induce a SigB-mediated increase in robustness, the presence of rpsUG50C in the ΔrsbV and the ΔrsbR1 mutants led to SigB activation and concomitant increased robustness, indicating an alternative signaling pathway for the SigB activation in rpsUG50C mutants. Interestingly, all these rpsUG50C mutants exhibited reduced maximum specific growth rates, independent of SigB activation, possibly attributed to compromised ribosomal functioning. In summary, the increased stress resistance in the L. monocytogenes EGDe rpsUG50C mutant results from SigB activation through an unknown mechanism distinct from the classical stressosome and RsbV/RsbW partner switching model. Moreover, the reduced maximum specific growth rate of the EGDe rpsUG50C mutant is likely unrelated to SigB activation and potentially linked to impaired ribosomal function.

Time-Lapse Imaging of Migrating Neurons and Glial Progenitors in Embryonic Mouse Brain Slices.

During the development of the cerebral cortex, neurons and glial cells originate in the ventricular zone lining the ventricle and migrate toward the brain surface. This process is crucial for proper brain function, and its dysregulation can result in neurodevelopmental and psychiatric disorders after birth. In fact, many genes responsible for these diseases have been found to be involved in this process, and therefore, revealing how these mutations affect cellular dynamics is important for understanding the pathogenesis of these diseases. This protocol introduces a technique for time-lapse imaging of migrating neurons and glial progenitors in brain slices obtained from mouse embryos. Cells are labeled with fluorescent proteins using in utero electroporation, which visualizes individual cells migrating from the ventricular zone with a high signal-to-noise ratio. Moreover, this in vivo gene transfer system enables us to easily perform gain-of-function or loss-of-function experiments on the given genes by co-electroporation of their expression or knockdown/knockout vectors. Using this protocol, the migratory behavior and migration speed of individual cells, information that is never obtained from fixed brains, can be analyzed.

The Photoprotective Behavior of a Motile Benthic Diatom as Elucidated from the Interplay Between Cell Motility and Physiological Responses to a Light Microgradient Using a Novel Experimental Setup

It has long been hypothesized that benthic motile pennate diatoms use phototaxis to optimize photosynthesis and minimize photoinhibitory damage by adjusting their position within vertical light gradients in coastal benthic sediments. However, experimental evidence to test this hypothesis remains inconclusive, mainly due to methodological difficulties in studying cell behavior and photosynthesis over realistic spatial microscale gradients of irradiance and cell position. In this study, a novel experimental approach was developed and used to test the hypothesis of photosynthesis optimization through motility, based on the combination of single-cell in vivo chlorophyll fluorometry and microfluidic chips. The approach allows the concurrent study of behavior and photosynthetic activity of individual cells of the epipelic diatom species Craspedostauros britannicus exposed to a light microgradient of realistic dimensions, simulating the irradiance and distance scales of light microgradients in benthic sediments. Following exposure to light, (i) cells explored their light environment before initiating light-directed motility; (ii) cells used motility to lower their light dose, when exposed to the highest light intensities; and (iii) motility was combined with reversible non-photochemical quenching, to allow cells to avoid photoinhibition. The results of this proof-of-concept study not only strongly support the photoprotective nature of photobehavior in the studied species but also revealed considerable variability in how individual cells reacted to a light microgradient. The experimental setup can be readily applied to study motility and photosynthetic light responses of other diatom species or natural assemblages, as well as other photoautotrophic motile microorganisms, broadening the toolset for experimental microbial ecology research.

Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification.

Singularity biology is a scientific field that targets drastic state changes in multicellular systems, aiming to discover the key cells that induce the state change and investigate the mechanisms behind them. To achieve this goal, we developed a trans-scale optical imaging system (trans-scale scope), that is capable of capturing both macroscale changes across the entire system and the micro-scale behavior of individual cells, surpassing the cell observation capabilities of traditional microscopes. We developed two units of the trans-scale scope, named AMATERAS-1 and -2, which demonstrated the ability to observe multicellular systems consisting of over one million cells in a single field of view with sub-cellular resolution. This flagship instrument has been used to observe the dynamics of various cell species, with the advantage of being able to observe a large number of cells, allowing the detection and analysis of rare events and cells such as leader cells in multicellular pattern formation and cells that spontaneously initiate calcium waves. In this paper, we present the design concept of AMATERAS, the optical configuration, and several examples of observations, and demonstrate how the strength-in-numbers works in life sciences.

Cell to Pack Modelling of Li-Ion Batteries for Electric Vehicle Applications: Development and Potential Challenges

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology.

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

Cell size: Single cells illuminate the rules of cell size control

A new study uses Chlamydomonas reinhardtii to understand how cell size homeostasis emerges from stochastic individual cell behaviors within a population. The authors find that a simple power law model was a poor predictor of cell size regulation; rather, it is better explained by a modified threshold model.

STEM-22. SINGLE-CELL LINEAGE TRACING IN PRIMARY GLIOBLASTOMA REVEALS DISTINCT PROGENITOR SUBTYPES DRIVING INTRATUMORAL HETEROGENEITY

Abstract Glioblastoma (GBM) is the most common type of adult primary brain tumor with nearly a 100% rate of recurrence. One of the key clinical challenges is the vast degree of inter- and intra-tumoral variation in cell types and cell states, which change throughout the course of disease. Single-cell RNA-sequencing (scRNA-seq) studies in GBM samples have illuminated the complex landscape of cell type composition in primary and recurrent GBM, however these studies do not address how this diverse array of cell types developed or which cell populations are principally responsible for generating the heterogeneity and treatment resistance in the tumor. In this study, we implement a scRNA-seq compatible lineage tracing strategy, CellTagging, to track the behavior of individual tumor cells over time. Human GBM samples are dissociated immediately upon resection and infected with the CellTag DNA barcode library. The labeled cells are transplanted onto human cortical organoids, which facilitate the crucial normal-tumor cell interactions that enable GBM growth and invasion. After a period of proliferation, tumor cells are dissociated from the organoid and a fraction are harvested for scRNA-seq for transcriptomic and DNA barcode analysis. The remaining tumor cells are re-transplanted onto new organoids and harvested for scRNA-seq at later timepoints. Clonal relationships are determined by identifying cells with the same DNA barcode profile, and cell type characterizations are assigned via transcriptomic analysis. Transcriptomic profiling demonstrates the existence of multiple subtypes of tumor progenitors across timepoints. Strikingly, clonal analysis indicates that each progenitor subtype gives rise to numerous mature tumor cell types, suggesting multiple distinct lineages that contribute to tumor heterogeneity. Through differential expression analysis, we can predict which progenitors are most likely to drive which cell states. By introducing a treatment paradigm into our lineage tracing protocol, we identify tumor progenitor populations that contribute to treatment resistance.

A cell-based model for size control in the multiple fission alga Chlamydomonas reinhardtii

Understanding how population-size homeostasis emerges from stochastic individual cell behaviors remains a challenge in biology.1,2,3,4,5,6,7 The unicellular green alga Chlamydomonas reinhardtii (Chlamydomonas) proliferates using a multiple fission cell cycle, where a prolonged G1 phase is followed by n rounds of alternating division cycles (S/M) to produce 2n daughters. A "Commitment" sizer in mid-G1 phase ensures sufficient cell growth before completing the cell cycle. A mitotic sizer couples mother-cell size to division number (n) such that daughter size distributions are uniform regardless of mother size distributions. Although daughter size distributions were highly robust to altered growth conditions, ∼40% of daughter cells fell outside of the 2-fold range expected from a "perfect" multiple fission sizer.7,8 A simple intuitive power law model with stochastic noise failed to reproduce individual division behaviors of tracked single cells. Through additional iterative modeling, we identified an alternative modified threshold (MT) model, where cells need to cross a threshold greater than 2-fold their median starting size to become division-competent (i.e., Committed), after which their behaviors followed a power law model. The Commitment versus mitotic size threshold uncoupling in the MT model was likely a key pre-adaptation in the evolution of volvocine algal multicellularity. A similar experimental approach was used in size mutants mat3/rbr and dp1 that are, respectively, missing repressor or activator subunits of the retinoblastoma tumor suppressor complex (RBC). Both mutants showed altered relationships between Commitment and mitotic sizer, suggesting that RBC functions to decouple the two sizers.

Agent-based approach for elucidating the release from collective arrest of cell motion in corneal epithelial cell sheet

Changes in cell fluidity have been observed in various cellular tissues and are strongly linked to biological phenomena such as self-organization. Recent studies suggested variety of mechanisms and factors, which are still being investigated. This study aimed to investigate changes in cell fluidity in multi-layered cell sheets, by exploring the collective arrest of cell motion and its release in cultures of corneal epithelial cells. We constructed mathematical models to simulate the behaviors of individual cells, including cell differentiation and time-dependent changes in cell-cell connections, which are defined by stochastic or kinetic rules. Changes in cell fluidity and cell sheet structures were expressed by simulating autonomous cell behaviors and interactions in tissues using an agent-based model. A single-cell level spatiotemporal analysis of cell state transition between migratable and non-migratable states revealed that the release from collective arrest of cell motion was initially triggered by a decreased ability to form cell-cell connections in the suprabasal layers, and was propagated by chain migration. Notably, the disruption of cell-cell connections and stratification occurred in the region of migratable state cells. Hence, a modeling approach that considers time-dependent changes in cell properties and behavior, and spatiotemporal analysis at the single-cell level can effectively delineate emergent phenomena arising from the complex interplay of cells.