Cells exhibit a mysterious form of selective heritable short-term memory, influencing outcomes as diverse as cell fate decisions in embryos and environmental responses in cancer cells and bacteria. Here, we present a simple theoretical framework explaining how this selective memory can arise from the reactions regulating molecular levels in cells. Our key insight is that related cells retain more similar molecular concentrations relative to random cells when a greater variance of possible concentration states is created during a single cell generation than is created by cell division across a population. This persistence of molecular similarity down a lineage constitutes a form of heritable short-term memory. We identify the biochemical networks that produce, modify, and degrade molecules as an underexplored source of these additional molecular concentration states. Using experimentally informed simulations, we find that the strength and duration of molecular similarity down a lineage depend on tunable network properties, explaining why some cellular traits persist only briefly while others last generations. These contributions to molecular concentration variance from biochemical reaction networks act in concert with gene expression and other regulatory processes to shape the protein composition of cells. Our framework yields clear, testable predictions for determining how biochemical network architectures drive non-genetic cellular inheritance.

In cancer progression, tumor microenvironments progressively become denser and hypoxic, and cell migrate toward higher oxygen levels as they invade across the tumor-stromal boundary. While cell invasion dependence on optimal collagen density is well appreciated, it remains unclear whether past oxygen conditions alter future invasion phenotype of cells. Here, we show that normal human mammary epithelial cells (MCF10A) and leader-like human breast tumor cells (BT549) undergo higher rates of invasion and collagen deformation after past exposure to hypoxia, compared to normoxia controls. Upon increasing collagen density by ∼50%, cell invasion under normoxia reduced, as expected due to the increased matrix crowding. However, surprisingly, past hypoxia increased cell invasion in future normoxic dense collagen, with more pronounced invasion of cancer cells. This culmination of cancer-related conditions of hypoxia history, tumor cell, and denser collagen led to more aggressive invasion phenotypes. We found that hypoxia-primed cancer cells produce laminin332, a basement membrane protein required for cell-matrix adhesions, which could explain the additional adhesion feedback from the matrix that led to invasion after hypoxia priming. Depletion of Cdh3 disrupts the hypoxia-dependent laminin production and thus disables the rise in rates of cancer cell invasion and collagen deformation caused by hypoxia memory. These findings highlight the importance of considering past oxygen conditions in combination with current mechanical composition of tissues to better understand tumor invasion in physically evolving tumor microenvironments.

This paper describes a platform for cooling microfluidic chips so as to freeze aqueous droplets flowing in oil. Using a whole-chip cooling chamber, we can control the ambient temperature surrounding a microfluidic chip and induce cooling and freezing inside the channels. When combined with a droplet generation and droplet docking chip, this platform allows for the facile freezing of droplets immobilized in resistance-based docks. Depending on the design and shape of the docks, the frozen droplets can either be trapped stably in the docks or be released because deformed non-frozen aqueous droplets turn spherical when frozen, and thus can become dislodged from the docks. Additionally, using this chamber and chip combination we are able to exchange immiscible phases and surfactants surrounding the frozen droplets. The materials and methods are inexpensive and easily accessible to microfluidics researchers, making this a simple addition to an existing microfluidic platform.

Interplay between models and experimental data advances discovery and understanding in biology, particularly when models generate predictions that allow well-designed experiments to distinguish between alternative mechanisms. To illustrate how this feedback between models and experiments can lead to key insights into biological mechanisms, we explore three examples from cellular slime mold chemotaxis. These examples include studies that identified chemotaxis as the primary mechanism behind slime mold aggregation, discovered that cells likely measure chemoattractant gradients by sensing concentration differences across cell length, and tested the role of cell-associated chemoattractant degradation in shaping chemotactic fields. Although each study used a different model class appropriate to their hypotheses - qualitative, mathematical, or simulation-based - these examples all highlight the utility of modeling to formalize assumptions and generate testable predictions. A central element of this framework is the iterative use of models and experiments, specifically: matching experimental designs to the models, revising models based on mismatches with experimental data, and validating critical model assumptions and predictions with experiments. We advocate for continued use of this interplay between models and experiments to advance biological discovery.

While the field of synthetic developmental biology has traditionally focused on the study of the rich developmental processes seen in metazoan systems, an attractive alternate source of inspiration comes from microbial developmental models. Microbes face unique lifestyle challenges when forming emergent multicellular collectives. As a result, the solutions they employ can inspire the design of novel multicellular systems. In this review, we dissect the strategies employed in multicellular development by two model microbial systems: the cellular slime mold Dictyostelium discoideum and the biofilm-forming bacterium Bacillus subtilis. Both microbes face similar challenges but often have different solutions, both from metazoan systems and from each other, to create emergent multicellularity. These challenges include assembling and sustaining a critical mass of participating individuals to support development, regulating entry into development, and assigning cell fates. The mechanisms these microbial systems exploit to robustly coordinate development under a wide range of conditions offer inspiration for a new toolbox of solutions to the synthetic development community. Additionally, recreating these phenomena synthetically offers a pathway to understanding the key principles underlying how these behaviors are be coordinated naturally.

[This corrects the article on p. 303 in vol. 12, PMID: 33520386.].

Interactions between the actin cytoskeleton and the plasma membrane are important in many eukaryotic cellular processes. During these processes, actin structures deform the cell membrane outward by applying forces parallel to the fiber's major axis (as in migration) or they deform the membrane inward by applying forces perpendicular to the fiber's major axis (as in the contractile ring during cytokinesis). Here we describe a novel actin-membrane interaction in human dermal myofibroblasts. When labeled with a cytosolic fluorophore, the myofibroblasts displayed prominent fluorescent structures on the ventral side of the cell. These structures are present in the cell membrane and colocalize with ventral actin stress fibers, suggesting that the stress fibers bend the membrane to form a "cytosolic pocket" that the fluorophores diffuse into, creating the observed structures. The existence of this pocket was confirmed by transmission electron microscopy. While dissolving the stress fibers, inhibiting fiber protein binding, or inhibiting myosin II binding of actin removed the observed pockets, modulating cellular contractility did not remove them. Taken together, our results illustrate a novel actin-membrane bending topology where the membrane is deformed outward rather than being pinched inward, resembling the topological inverse of the contractile ring found in cytokinesis.

Nonlinear oscillator systems are ubiquitous in biology and physics, and their control is a practical problem in many experimental systems. Here we study this problem in the context of the two models of spatially coupled oscillators: the complex Ginzburg-Landau equation (CGLE) and a generalization of the CGLE in which oscillators are coupled through an external medium (emCGLE). We focus on external control drives that vary in both space and time. We find that the spatial distribution of the drive signal controls the frequency ranges over which oscillators synchronize to the drive and that boundary conditions strongly influence synchronization to external drives for the CGLE. Our calculations also show that the emCGLE has a low density regime in which a broad range of frequencies can be synchronized for low drive amplitudes. We study the bifurcation structure of these models and find that they are very similar to results for the driven Kuramoto model, a system with no spatial structure. We conclude by discussing qualitative implications of our results for controlling coupled oscillator systems such as the social amoebae Dictyostelium and populations of Belousov Zhabotinsky (BZ) catalytic particles using spatially structured external drives.

Label-free vibrational imaging of biological samples has attracted significant interest due to its integration of structural and chemical information. Vibrational infrared photothermal amplitude and phase signal (VIPPS) imaging provide label-free chemical identification by targeting the characteristic resonances of biological compounds that are present in the mid-infrared fingerprint region (3 µm - 12 µm). High contrast imaging of subcellular features and chemical identification of protein secondary structures in unlabeled and labeled fibroblast cells embedded in a collagen-rich extracellular matrix is demonstrated by combining contrast from absorption signatures (amplitude signals) with sensitive detection of different heat properties (lock-in phase signals). We present that the detectability of nano-sized cell membranes is enhanced to well below the optical diffraction limit since the membranes are found to act as thermal barriers. VIPPS offers a novel combination of chemical imaging and thermal diffusion characterization that paves the way towards label-free imaging of cell models and tissues as well as the study of intracellular heat dynamics.

Tissue repair is a complex process that requires effective communication and coordination between cells across multiple tissues and organ systems. Two of the initial intracellular signals that encode injury signals and initiate tissue repair responses are calcium and extracellular signal-regulated kinase (ERK). However, calcium and ERK signaling control a variety of cellular behaviors important for injury repair including cellular motility, contractility, and proliferation, as well as the activity of several different transcription factors, making it challenging to relate specific injury signals to their respective repair programs. This knowledge gap ultimately hinders the development of new wound healing therapies that could take advantage of native cellular signaling programs to more effectively repair tissue damage. The objective of this review is to highlight the roles of calcium and ERK signaling dynamics as mechanisms that link specific injury signals to specific cellular repair programs during epithelial and stromal injury repair. We detail how the signaling networks controlling calcium and ERK can now also be dissected using classical signal processing techniques with the advent of new biosensors and optogenetic signal controllers. Finally, we advocate the importance of recognizing calcium and ERK dynamics as key links between injury detection and injury repair programs that both organize and execute a coordinated tissue repair response between cells across different tissues and organs. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling Laboratory Methods and Technologies > Imaging Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.