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.

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.

Cells work together to accomplish complex tasks. For example, both neutrophils and Dictyostelid collectives use self-generated multicellular signaling gradients to coordinate aggregation over large areas through local interactions. However, these aggregation programs occur for different reasons that necessitate different implementations. Dictyostelids are soil-dwelling amoeba that aggregate when starving to facilitate dispersal to new locations. These aggregates do not require specific locations or group sizes. In contrast, neutrophils are innate immune cells that collectively migrate to sites of injury and infection. These swarms need to occur in specific locations and must be constrained in size to avoid collateral damage to the host. Here, we review how these evolutionarily divergent systems sculpt long-range gradients at the molecular and cellular levels, discussing their similarities and differences in light of their distinctive goals. Convergence on self-generated gradients for aggregation despite different goals suggests that it is an optimal strategy to bring individuals together in complex environments.

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.

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.

Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales-from biochemical networks within individual cells to spatially structured cellular populations. Here we present a family of "multiscale" models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cyclic adenosine monophosphate (cAMP) used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various preprocessing modules. We use this family of models to study spatially unstructured populations of "fixed" cells by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then briefly discuss an extension of our model that includes spatial structure and show how this naturally gives rise to spiral waves. Our models exhibit a wide range of novel phenomena. including a density-dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.

Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.

Axonal transport of synaptic vesicle proteins is required to maintain neurons' ability to communicate via synaptic transmission. Neurotransmitter-containing synaptic vesicles are assembled at synaptic terminals via highly regulated endocytosis of membrane proteins. These synaptic vesicle membrane proteins are synthesized in the cell body and transported to synapses in carrier vesicles that make their way down axons via microtubule-based transport utilizing kinesin molecular motors. Identifying the cargos that each kinesin motor protein carries from the cell bodies to the synapse is key to understanding both diseases caused by motor protein dysfunction and how synaptic vesicles are assembled. However, obtaining a bulk sample of axonal transport complexes from central nervous system (CNS) neurons to use for identification of their contents has posed a challenge to researchers. To obtain axonal carrier vesicles from primary cultured neurons, we fabricated a microfluidic chip designed to physically isolate axons from dendrites and cell bodies and developed a method to remove bulk axonal samples and label their contents. Synaptic vesicle protein carrier vesicles in these samples were labeled with antibodies to the synaptic vesicle proteins p38, SV2A, and VAMP2, and the anterograde axonal transport motor KIF1A, after which antibody overlap was evaluated using single-organelle TIRF microscopy. This work confirms a previously discovered association between KIF1A and p38 and shows that KIF1A also transports SV2A- and VAMP2-containing carrier vesicles.

Synapses are individually operated, computational units for neural communication. To manipulate physically individual synapses in a living organism, we have developed a laser ablation technique for removing single synapses in live neurons in C. elegans that operates without apparent damage to the axon. As a complementary technique, we applied microfluidic immobilization of C. elegans to facilitate long-term fluorescence imaging and observation of neuronal development. With this technique, we directly demonstrated the existence of competition between developing synapses in the HSNL motor neuron.

No abstract available.