LapTrack: linear assignment particle tracking with tunable metrics.

MOTIVATION: Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. RESULTS: We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation and visualization. AVAILABILITY AND IMPLEMENTATION: LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gradual differentiation uncoupled from cell cycle exit generates heterogeneity in the epidermal stem cell layer.

Highly regenerative tissues continuously produce terminally differentiated cells to replace those that are lost. How they orchestrate the complex transition from undifferentiated stem cells towards post-mitotic, molecularly distinct and often spatially segregated differentiated populations is not well understood. In the adult skin epidermis, the stem cell compartment contains molecularly heterogeneous subpopulations1-4 whose relationship to the complete trajectory of differentiation remains unknown. Here we show that differentiation, from commitment to exit from the stem cell layer, is a multi-day process wherein cells transit through a continuum of transcriptional changes with upregulation of differentiation genes preceding downregulation of typical stemness genes. Differentiation-committed cells remain capable of dividing to produce daughter cells fated to further differentiate, demonstrating that differentiation is uncoupled from cell cycle exit. These cell divisions are not required as part of an obligate transit-amplifying programme but help to buffer the differentiating cell pool during heightened demand. Thus, instead of distinct contributions from multiple progenitors, a continuous gradual differentiation process fuels homeostatic epidermal turnover.

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.

Surface wetting by kinetic control of liquid-liquid phase separation.

Motivated by the observations of intracellular phase separations and the wetting of cell membranes by protein droplets, we study the nonequilibrium surface wetting by Monte Carlo simulations of a lattice gas model involving particle creation. We find that, even when complete wetting should occur in equilibrium, the fast creation of particles can hinder the surface wetting for a long time due to the bulk droplet formation. Performing molecular dynamics simulations, we show that this situation also holds in colloidal particle systems when the disorder density is sufficiently high. The results suggest an intracellular control mechanism of surface wetting by changing the speed of component synthesis.

Introductions to the Community: Early-Career Researchers in the Time of COVID-19.

COVID-19 has unfortunately halted lab work, conferences, and in-person networking, which is especially detrimental to researchers just starting their labs. Through social media and our reviewer networks, we met some early-career stem cell investigators impacted by the closures. Here, they introduce themselves and their research to our readers.

A large pool of actively cycling progenitors orchestrates self-renewal and injury repair of an ectodermal appendage.

The classical model of tissue renewal posits that small numbers of quiescent stem cells (SCs) give rise to proliferating transit-amplifying cells before terminal differentiation. However, many organs house pools of SCs with proliferative and differentiation potentials that diverge from this template. Resolving SC identity and organization is therefore central to understanding tissue renewal. Here, using a combination of single-cell RNA sequencing (scRNA-seq), mouse genetics and tissue injury approaches, we uncover cellular hierarchies and mechanisms that underlie the maintenance and repair of the continuously growing mouse incisor. Our results reveal that, during homeostasis, a group of actively cycling epithelial progenitors generates enamel-producing ameloblasts and adjacent layers of non-ameloblast cells. After injury, tissue repair was achieved through transient increases in progenitor-cell proliferation and through direct conversion of Notch1-expressing cells to ameloblasts. We elucidate epithelial SC identity, position and function, providing a mechanistic basis for the homeostasis and repair of a fast-turnover ectodermal appendage.

Chromatin state switching in a polymer model with mark-conformation coupling.

We investigate the phase transition properties of the polymer-Potts model, a chain composed of monomers with magnetic degrees of freedom, with the motivation to study the conformation and mark switching dynamics of chromatin. By the mean-field approximation, we find that the phase transition between the swollen-disordered state and the compact-ordered state is discrete; it is first order as in the long-range Potts model, but with a significantly larger jump in magnetization (i.e., mark coherence) upon the ordering transition. The results imply how small changes in epigenetic writer concentrations can lead to a macroscopic switching of the chromatin state, suggesting a simple mechanism of discrete switching observed, for instance, in cell differentiation.

Homeostatic Epidermal Stem Cell Self-Renewal Is Driven by Local Differentiation.

Maintenance of adult tissues depends on sustained activity of resident stem cell populations, but the mechanisms that regulate stem cell self-renewal during homeostasis remain largely unknown. Using an imaging and tracking approach that captures all epidermal stem cell activity in large regions of living mice, we show that self-renewal is locally coordinated with epidermal differentiation, with a lag time of 1 to 2 days. In both homeostasis and upon experimental perturbation, we find that differentiation of a single stem cell is followed by division of a direct neighbor, but not vice versa. Finally, we show that exit from the stem cell compartment is sufficient to drive neighboring stem cell self-renewal. Together, these findings establish that epidermal stem cell self-renewal is not the constitutive driver of homeostasis. Instead, it is precisely tuned to tissue demand and responds directly to neighbor cell differentiation.

Unattainability of Carnot efficiency in thermal motors: Coarse graining and entropy production of Feynman-Smoluchowski ratchets.

We revisit and analyze the thermodynamic efficiency of the Feynman-Smoluchowski (FS) ratchet, a classical thought experiment describing an autonomous heat-work converter. Starting from the full kinetics of the FS ratchet and deriving the exact forms of the hidden dissipations resulting from coarse graining, we restate the historical controversy over its thermodynamic efficiency. The existence of hidden entropy productions implies that the standard framework of stochastic thermodynamics applied to the coarse-grained descriptions fails in capturing the dissipative feature of the system. In response to this problem, we explore an extended framework of stochastic thermodynamics to reconstruct the hidden entropy production from the coarse-grained dynamics. The approach serves as a key example of how we can systematically address the problem of thermodynamic efficiency in a multivariable fluctuating nonequilibrium system.

Topological defects control collective dynamics in neural progenitor cell cultures.

Cultured stem cells have become a standard platform not only for regenerative medicine and developmental biology but also for biophysical studies. Yet, the characterization of cultured stem cells at the level of morphology and of the macroscopic patterns resulting from cell-to-cell interactions remains largely qualitative. Here we report on the collective dynamics of cultured murine neural progenitor cells (NPCs), which are multipotent stem cells that give rise to cells in the central nervous system. At low densities, NPCs moved randomly in an amoeba-like fashion. However, NPCs at high density elongated and aligned their shapes with one another, gliding at relatively high velocities. Although the direction of motion of individual cells reversed stochastically along the axes of alignment, the cells were capable of forming an aligned pattern up to length scales similar to that of the migratory stream observed in the adult brain. The two-dimensional order of alignment within the culture showed a liquid-crystalline pattern containing interspersed topological defects with winding numbers of +1/2 and -1/2 (half-integer due to the nematic feature that arises from the head-tail symmetry of cell-to-cell interaction). We identified rapid cell accumulation at +1/2 defects and the formation of three-dimensional mounds. Imaging at the single-cell level around the defects allowed us to quantify the velocity field and the evolving cell density; cells not only concentrate at +1/2 defects, but also escape from -1/2 defects. We propose a generic mechanism for the instability in cell density around the defects that arises from the interplay between the anisotropic friction and the active force field.

Dynamical crossover in a stochastic model of cell fate decision.

We study the asymptotic behaviors of stochastic cell fate decision between proliferation and differentiation. We propose a model of a self-replicating Langevin system, where cells choose their fate (i.e., proliferation or differentiation) depending on local cell density. Based on this model, we propose a scenario for multicellular organisms to maintain the density of cells (i.e., homeostasis) through finite-ranged cell-cell interactions. Furthermore, we numerically show that the distribution of the number of descendant cells changes over time, thus unifying the previously proposed two models regarding homeostasis: the critical birth death process and the voter model. Our results provide a general platform for the study of stochastic cell fate decision in terms of nonequilibrium statistical mechanics.

Entropy Production of Nanosystems with Time Scale Separation.

Energy flows in biomolecular motors and machines are vital to their function. Yet experimental observations are often limited to a small subset of variables that participate in energy transport and dissipation. Here we show, through a solvable Langevin model, that the seemingly hidden entropy production is measurable through the violation spectrum of the fluctuation-response relation of a slow observable. For general Markov systems with time scale separation, we prove that the violation spectrum exhibits a characteristic plateau in the intermediate frequency region. Despite its vanishing height, the plateau can account for energy dissipation over a broad time scale. Our findings suggest a general possibility to probe hidden entropy production in nanosystems without direct observation of fast variables.

Spatiotemporal coordination of stem cell commitment during epidermal homeostasis.

Adult tissues replace lost cells via pools of stem cells. However, the mechanisms of cell self-renewal, commitment, and functional integration into the tissue remain unsolved. Using imaging techniques in live mice, we captured the lifetime of individual cells in the ear and paw epidermis. Our data suggest that epidermal stem cells have equal potential to either divide or directly differentiate. Tracking stem cells over multiple generations reveals that cell behavior is not coordinated between generations. However, sibling cell fate and lifetimes are coupled. We did not observe regulated asymmetric cell divisions. Lastly, we demonstrated that differentiating stem cells integrate into preexisting ordered spatial units of the epidermis. This study elucidates how a tissue is maintained by both temporal and spatial coordination of stem cell behaviors.

Invariance of steady-state thermodynamics between different scales of description.

By considering general Markov stochastic dynamics and its coarse graining, we study the framework of stochastic thermodynamics for the original and reduced descriptions corresponding to different scales. We are especially concerned with the case where the irreversible entropy production has a finite difference between the scales. We find that the sum of the increment of the nonequilibrium entropy and the excess part of the entropy production, which are key quantities in the construction of steady-state thermodynamics, is essentially kept invariant with respect to the change in the scales of description. This general result justifies experimental approaches toward steady-state thermodynamics based on coarse-grained variables. We demonstrate our result in a mesoscopic heat engine system.

Nonequilibrium dissipation-free transport in F1-ATPase and the thermodynamic role of asymmetric allosterism.

F1-ATPase (or F1), the highly efficient and reversible biochemical engine, has motivated physicists as well as biologists to imagine the design principles governing machines in the fluctuating world. Recent experiments have clarified yet another interesting property of F1; the dissipative heat inside the motor is very small, irrespective of the velocity of rotation and energy transport. Conceptual interest is devoted to the fact that the amount of internal dissipation is not simply determined by the sequence of equilibrium pictures, but also relies on the rotational-angular dependence of nucleotide affinity, which is a truly nonequilibrium aspect. We propose that the totally asymmetric allosteric model (TASAM), where adenosine triphosphate (ATP) binding to F1 is assumed to have low dependence on the angle of the rotating shaft, produces results that are most consistent with the experiments. Theoretical analysis proves the crucial role of two time scales in the model, which explains the universal mechanism to produce the internal dissipation-free feature. The model reproduces the characteristic torque dependence of the rotational velocity of F1 and predicts that the internal dissipation upon the ATP synthesis direction rotation becomes large at the low nucleotide condition.

Oscillatory control of factors determining multipotency and fate in mouse neural progenitors.

The basic helix-loop-helix transcription factors Ascl1/Mash1, Hes1, and Olig2 regulate fate choice of neurons, astrocytes, and oligodendrocytes, respectively. These same factors are coexpressed by neural progenitor cells. Here, we found by time-lapse imaging that these factors are expressed in an oscillatory manner by mouse neural progenitor cells. In each differentiation lineage, one of the factors becomes dominant. We used optogenetics to control expression of Ascl1 and found that, although sustained Ascl1 expression promotes neuronal fate determination, oscillatory Ascl1 expression maintains proliferating neural progenitor cells. Thus, the multipotent state correlates with oscillatory expression of several fate-determination factors, whereas the differentiated state correlates with sustained expression of a single factor.

Fluctuation theorem for hidden entropy production.

Elimination of seemingly unnecessary variables in Markovian models may cause a difference in the value of irreversible entropy production between the original and reduced dynamics. We show that such difference, which we call the hidden entropy production, obeys an integral fluctuation theorem if all variables are time-reversal invariant, or if the density function is symmetric with respect to the change of sign of the time-reversal antisymmetric variables. The theorem has wide applicability, since the proposed condition is mostly satisfied in the case where the hidden fast variables are equilibrated. The main consequence of this theorem is that the entropy production decreases by the coarse-graining procedure. By contrast, in the case where a stochastic process is obtained by coarse-graining a deterministic and reversible dynamics, the entropy production may increase, implying that the integral fluctuation theorem should not hold for such reductions. We reveal, with an explicit example, that the nonequilibrated time-reversal antisymmetric variables play a crucial role in distinguishing these two cases, thus guaranteeing the consistency of the presented theorem.