Heterogeneous T cell motility behaviors emerge from a coupling between speed and turning in vivo

Mesenchymal cell migration on one-dimensional micropatterns

Quantitative studies of mesenchymal cell motion are important to elucidate cytoskeleton function and mechanisms of cell migration. To this end, confinement of cell motion to one dimension (1D) significantly simplifies the problem of cell shape in experimental and theoretical investigations. Here we review 1D migration assays employing micro-fabricated lanes and reflect on the advantages of such platforms. Data are analyzed using biophysical models of cell migration that reproduce the rich scenario of morphodynamic behavior found in 1D. We describe basic model assumptions and model behavior. It appears that mechanical models explain the occurrence of universal relations conserved across different cell lines such as the adhesion-velocity relation and the universal correlation between speed and persistence (UCSP). We highlight the unique opportunity of reproducible and standardized 1D assays to validate theory based on statistical measures from large data of trajectories and discuss the potential of experimental settings embedding controlled perturbations to probe response in migratory behavior.

Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction.

Migrate3D: Software for simplified post-tracking analysis of 3D and 2D cell migration data

Migrate3D is a cell migration analysis tool whose purpose is to computationally process positional cell tracking data generated via other image acquisition/analysis software and generate biologically meaningful results. The functionalities of Migrate3D include step-based calculations of each cell track, single-cell-level summary statistics, mean squared displacement analysis, and machine learning-based evaluation of the entire dataset and subpopulations of cells found within it. The parameters calculated within Migrate3D have been previously developed and validated by other groups, and were selected to facilitate extraction of the maximum depth of information possible from input datasets. Variables are user-adjustable to enable customized analyses of diverse motility patterns and cell types, both in three-and two-dimensional timelapse data. Independent of any particular upstream image analysis or cell tracking software, Migrate3D only needs positional data over time to execute the suite of calculations. This presents a unique opportunity to standardize and streamline cell migration analysis.

Fishing Innate Immune System Properties through the Transcriptomic Single-Cell Data of Teleostei

The innate immune system is the first line of defense in multicellular organisms. Danio rerio is widely considered a promising model for IIS-related research, with the most amount of scRNAseq data available among Teleostei. We summarized the scRNAseq and spatial transcriptomics experiments related to the IIS for zebrafish and other Teleostei from the GEO NCBI and the Single-Cell Expression Atlas. We found a considerable number of scRNAseq experiments at different stages of zebrafish development in organs such as the kidney, liver, stomach, heart, and brain. These datasets could be further used to conduct large-scale meta-analyses and to compare the IIS of zebrafish with the mammalian one. However, only a small number of scRNAseq datasets are available for other fish (turbot, salmon, cavefish, and dark sleeper). Since fish biology is very diverse, it would be a major mistake to use zebrafish alone in fish immunology studies. In particular, there is a special need for new scRNAseq experiments involving nonmodel Teleostei, e.g., long-lived species, cancer-resistant fish, and various fish ecotypes.

Motility of Mytilus galloprovincialis hemocytes: Sensitivity to paracetamol in vitro exposure

Pharmaceuticals released into the environment (PiEs) represent an environmental problem of growing concern for the health of ecosystems and humans. An increasing number of studies show that PiEs pose a risk to aquatic organisms. The aim of the present work was to contribute to increasing the knowledge of the effects of PiE on marine biota focusing on the effect of paracetamol on the motility of hemocytes in Mytilus galloprovincialis, a bivalve mollusk species widely utilized as bioindicator organism. Hemocytes are the immunocompetent cells of bivalve mollusks. An early and key stage of mollusk immune response is represented by the recruitment and migration of these cells to the site of infection. Therefore, motility is an intrinsic characteristic of these cells. Here, we first characterized the spontaneous cell movement of M. galloprovincialis hemocytes when plated in a TC-treated polystyrene 96-well microplate. Two different cellular morphotypes were distinguished based on their appearance and motility behavior: spread cells and round-star-shaped cells. The two motility morphotypes were characterized by different velocities as well as movement directness, which were significantly lower in round-star-shaped cells with respect to spread cells. The sensitivity of the motility of M. galloprovincialis hemocytes to paracetamol at different concentrations (0.02, 0.2 and 2 mg/L) was investigated in vitro after 1h and 24h exposure. Paracetamol induced alterations in the motility behavior (both velocity and trajectories) of the hemocytes and the effects were cell-type specific. The study of hemocyte movements at the single cell level by cell tracking and velocimetric parameters analysis provides new sensitive tools for assessing the effects of emerging pollutants at the cellular levels in non-target organisms.

Heterogeneous anomalous transport in cellular and molecular biology

It is well established that a wide variety of phenomena in cellular and molecular biology involve anomalous transport e.g. the statistics for the motility of cells and molecules are fractional and do not conform to the archetypes of simple diffusion or ballistic transport. Recent research demonstrates that anomalous transport is in many cases heterogeneous in both time and space. Thus single anomalous exponents and single generalised diffusion coefficients are unable to satisfactorily describe many crucial phenomena in cellular and molecular biology. We consider advances in the field ofheterogeneous anomalous transport(HAT) highlighting: experimental techniques (single molecule methods, microscopy, image analysis, fluorescence correlation spectroscopy, inelastic neutron scattering, and nuclear magnetic resonance), theoretical tools for data analysis (robust statistical methods such as first passage probabilities, survival analysis, different varieties of mean square displacements, etc), analytic theory and generative theoretical models based on simulations. Special emphasis is made on high throughput analysis techniques based on machine learning and neural networks. Furthermore, we consider anomalous transport in the context of microrheology and the heterogeneous viscoelasticity of complex fluids. HAT in the wavefronts of reaction-diffusion systems is also considered since it plays an important role in morphogenesis and signalling. In addition, we present specific examples from cellular biology including embryonic cells, leucocytes, cancer cells, bacterial cells, bacterial biofilms, and eukaryotic microorganisms. Case studies from molecular biology include DNA, membranes, endosomal transport, endoplasmic reticula, mucins, globular proteins, and amyloids.

Random walk and cell morphology dynamics in Naegleria gruberi

Amoeboid cell movement and migration are wide-spread across various cell types and species. Microscopy-based analysis of the model systems Dictyostelium and neutrophils over the years have uncovered generality in their overall cell movement pattern. Under no directional cues, the centroid movement can be quantitatively characterized by their persistence to move in a straight line and the frequency of re-orientation. Mathematically, the cells essentially behave as a persistent random walker with memory of two characteristic time-scale. Such quantitative characterization is important from a cellular-level ethology point of view as it has direct connotation to their exploratory and foraging strategies. Interestingly, outside the amoebozoa and metazoa, there are largely uncharacterized species in the excavate taxon Heterolobosea including amoeboflagellate Naegleria. While classical works have shown that these cells indeed show typical amoeboid locomotion on an attached surface, their quantitative features are so far unexplored. Here, we analyzed the cell movement of Naegleria gruberi by employing long-time phase contrast imaging that automatically tracks individual cells. We show that the cells move as a persistent random walker with two time-scales that are close to those known in Dictyostelium and neutrophils. Similarities were also found in the shape dynamics which are characterized by the appearance, splitting and annihilation of the curvature waves along the cell edge. Our analysis based on the Fourier descriptor and a neural network classifier point to importance of morphology features unique to Naegleria including complex protrusions and the transient bipolar dumbbell morphologies.

Quantitative analyses of T cell motion in tissue reveals factors driving T cell search in tissues

T cells are required to clear infection, and T cell motion plays a role in how quickly a T cell finds its target, from initial naive T cell activation by a dendritic cell to interaction with target cells in infected tissue. To better understand how different tissue environments affect T cell motility, we compared multiple features of T cell motion including speed, persistence, turning angle, directionality, and confinement of T cells moving in multiple murine tissues using microscopy. We quantitatively analyzed naive T cell motility within the lymph node and compared motility parameters with activated CD8 T cells moving within the villi of small intestine and lung under different activation conditions. Our motility analysis found that while the speeds and the overall displacement of T cells vary within all tissues analyzed, T cells in all tissues tended to persist at the same speed. Interestingly, we found that T cells in the lung show a marked population of T cells turning at close to 180o, while T cells in lymph nodes and villi do not exhibit this "reversing" movement. T cells in the lung also showed significantly decreased meandering ratios and increased confinement compared to T cells in lymph nodes and villi. These differences in motility patterns led to a decrease in the total volume scanned by T cells in lung compared to T cells in lymph node and villi. These results suggest that the tissue environment in which T cells move can impact the type of motility and ultimately, the efficiency of T cell search for target cells within specialized tissues such as the lung.

Brain-localized CD4 and CD8 T cells perform correlated random walks and not Levy walks

Background. For survival of the organism, T cells must efficiently control pathogens invading different peripheral tissues. Whether or not such control is achieved by utilizing different movement strategies in different tissues remains poorly understood. Liver-localized CD8 T cells perform correlated random walks  --- a type of a Brownian walk -- in liver sinusoids but in some condition these T cells may also perform Levy flights -- rapid and large displacements by floating with the blood flow. CD8 T cells in lymph nodes or skin also undergo Brownian walks. A recent study suggested that brain-localized CD8 T cells, specific to Toxoplasma gondii, perform generalized Levy walks -- a walk type in which T cells alternate pausing and displacing long distances --- which may indicate that brain is a unique organ where T cells exhibit movement strategies different from other tissues. Methods.  We quantified movement patterns of brain-localized Plasmodium berghei-specific CD4 and CD8 T cells by using well-established statistical and computational methods. Results.  We found that T cells change their movement pattern with time since infection and that CD4 T cells move faster and turn less than CD8 T cells. Importantly, both CD4 and CD8 T cells move in the brain by correlated random walks without long displacements challenging previous observations. We have also re-analyzed the movement data of brain-localized CD8 T cells in T. gondii-infected mice and found no evidence of Levy walks. We hypothesize that the previous conclusion of Levy walks of T. gondii-specific CD8 T cells in the brain was reached due to missing time-frames in the data that create an impression of large movement lengths between assumed-to-be-sequential movements.  Conclusion. Our results suggests that movement strategies of CD8 T cells are largely similar between LNs, liver, and the brain and consistent with correlated random walks and not Levy walks.

Adhesion tunes speed and persistence by coordinating protrusions and extracellular matrix remodeling

Cell migration through 3D environments is essential to development, disease, and regeneration processes. Conceptual models of migration have been developed primarily on the basis of 2D cell behaviors, but a general understanding of 3D cell migration is still lacking due to the added complexity of the extracellular matrix. Here, using a multiplexed biophysical imaging approach for single-cell analysis of human cell lines, we show how the subprocesses of adhesion, contractility, actin cytoskeletal dynamics, and matrix remodeling integrate to produce heterogeneous migration behaviors. This single-cell analysis identifies three modes of cell speed and persistence coupling, driven by distinct modes of coordination between matrix remodeling and protrusive activity. The framework that emerges establishes a predictive model linking cell trajectories to distinct subprocess coordination states.

A tessellated lymphoid network provides whole-body T cell surveillance in zebrafish

Homeostatic trafficking to lymph nodes allows T cells to efficiently survey the host for cognate antigen. Nonmammalian jawed vertebrates lack lymph nodes but maintain diverse T cell pools. Here, we exploit in vivo imaging of transparent zebrafish to investigate how T cells organize and survey for antigen in an animal devoid of lymph nodes. We find that naïve-like T cells in zebrafish organize into a previously undescribed whole-body lymphoid network that supports streaming migration and coordinated trafficking through the host. This network has the cellular hallmarks of a mammalian lymph node, including naïve T cells and CCR7-ligand expressing nonhematopoietic cells, and facilitates rapid collective migration. During infection, T cells transition to a random walk that supports antigen-presenting cell interactions and subsequent activation. Our results reveal that T cells can toggle between collective migration and individual random walks to prioritize either large-scale trafficking or antigen search in situ. This lymphoid network thus facilitates whole-body T cell trafficking and antigen surveillance in the absence of a lymph node system.

Interpreting T-cell search "strategies" in the light of evolution under constraints

Two decades of in vivo imaging have revealed how diverse T-cell motion patterns can be. Such recordings have sparked the notion of search "strategies": T cells may have evolved ways to search for antigen efficiently depending on the task at hand. Mathematical models have indeed confirmed that several observed T-cell migration patterns resemble a theoretical optimum; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell's chance of finding antigen. But the same behaviours could also arise simply because T cells cannot follow a straight, regular path through the tight spaces they navigate. Even if T cells do follow a theoretically optimal pattern, the question remains: which parts of that pattern have truly been evolved for search, and which merely reflect constraints from the cell's migration machinery and surroundings? We here employ an approach from the field of evolutionary biology to examine how cells might evolve search strategies under realistic constraints. Using a cellular Potts model (CPM), where motion arises from intracellular dynamics interacting with cell shape and a constraining environment, we simulate evolutionary optimization of a simple task: explore as much area as possible. We find that our simulated cells indeed evolve their motility patterns. But the evolved behaviors are not shaped solely by what is functionally optimal; importantly, they also reflect mechanistic constraints. Cells in our model evolve several motility characteristics previously attributed to search optimisation-even though these features are not beneficial for the task given here. Our results stress that search patterns may evolve for other reasons than being "optimal". In part, they may be the inevitable side effects of interactions between cell shape, intracellular dynamics, and the diverse environments T cells face in vivo.

IL18 Receptor Signaling Inhibits Intratumoral CD8+ T-Cell Migration in a Murine Pancreatic Cancer Model

In pancreatic ductal adenocarcinoma (PDAC), the infiltration of CD8⁺ cytotoxic T cells (CTLs) is an important factor in determining prognosis. The migration pattern and interaction behavior of intratumoral CTLs are pivotal to tumor rejection. NLRP3-dependent proinflammatory cytokines IL-1β and IL-18 play a prominent role for CTL induction and differentiation. Here, we investigate the effects of T-cellular IL-1R and IL-18R signaling for intratumoral T-cell motility. Murine adenocarcinoma cell line Panc02 was stably transfected with ovalbumin (OVA) and fluorophore H2B-Cerulean to generate PancOVA H2B-Cerulean tumor cells. Dorsal skinfold chambers (DSFC) were installed on wild-type mice, and PancOVA H2B-Cerulean tumor cells were implanted into the chambers. PancOVA spheroids were formed using the Corning^® Matrigel^®-based 3D cell culture technique. CTLs were generated from OT-1 mice, Il1r^-/- OT-1 mice, or Il18r^-/- OT-1 mice and were marked with fluorophores. This was followed by the adoptive transfer of CTLs into tumor-bearing mice or the application into tumor spheroids. After visualization with multiphoton microscopy (MPM), Imaris software was used to perform T-cell tracking. Imaris analysis indicates a significantly higher accumulation of Il18r^-/- CTLs in PancOVA tumors and a significant reduction in tumor volume compared to wild-type CTLs. Il18r^-/- CTLs covered a longer distance (track displacement length) in comparison to wild-type (WT) CTLs, and had a higher average speed (mean track speed). The analysis of instantaneous velocity suggests a higher percentage of arrested tracks (arrests: <4 μm/min) for Il18r^-/- CTLs. Our data indicate the contribution of IL-18R signaling to T-cell effector strength, warranting further investigation on phenomena such as intratumoral T-cell exhaustion.

Correlation between speed and turning naturally arises for sparsely sampled cell movements

Mechanisms regulating cell movement are not fully understood. One feature of cell movement that determines how far cells displace from an initial position is persistence, the ability to perform movements in a direction similar to the previous movement direction. Persistence is thus determined by turning angles (TA) between two sequential displacements and can be characterized by an average TA or persistence time. Recent studies documenting T cell movement in zebrafish found that a cell's average speed and average TA are negatively correlated, suggesting a fundamental cell-intrinsic program whereby cells with a lower TA (and larger persistence time) are intrinsically faster (or faster cells turn less). In this paper we confirm the existence of the correlation between turning and speed for six different datasets on 3D movement of CD8 T cells in murine lymph nodes or liver. Interestingly, the negative correlation between TA and speed was observed in experiments in which liver-localized CD8 T cells rapidly displace due to floating with the blood flow, suggesting that other mechanisms besides cell-intrinsic program may be at play. By simulating correlated random walks using two different frameworks (one based on the von Mises-Fisher (vMF) distribution and another based on the Ornstein-Uhlenbeck (OU) process) we show that the negative correlation between speed and turning naturally arises when cell trajectories are sub-sampled, i.e. when the frequency of sampling is lower than frequency at which cells typically make movements. This effect is strongest when the sampling frequency is of the order of magnitude of the inverse of persistence time of cells and when cells vary in persistence time. The effect arises in part due to the sensitivity of estimated cell speeds to the frequency of imaging whereby less frequent imaging results in slower speeds. Interestingly, by using estimated persistence times for cells in two of our datasets and simulating cell movements using the OU process, we could partially reproduce the experimentally observed correlation between TA and speed without a cell-intrinsic program linking the two processes. Our results thus suggest that sub-sampling may contribute to (and perhaps fully explains) the observed correlation between speed and turning at least for some cell trajectory data and emphasize the role of sampling frequency in the inference of critical cellular parameters of cell motility such as speeds.

A tessellated lymphoid network provides whole-body T cell surveillance in zebrafish

Homeostatic trafficking to lymph nodes allows T cells to efficiently survey the host for cognate antigen. Non-mammalian jawed vertebrates lack lymph nodes but maintain similarly diverse T cell pools. Here, we exploit in vivo imaging of transparent zebrafish to investigate how T cells organize and survey for antigen in an animal devoid of lymph nodes. We find that naïve-like T cells in zebrafish organize into a previously undescribed whole-body lymphoid network that supports streaming migration and coordinated trafficking through the host. This network has the cellular hallmarks of a mammalian lymph node, including naïve T cells and CCR7-ligand expressing non-hematopoietic cells, and facilitates rapid collective migration. During infection, T cells transition to a random walk that supports antigen presenting cell interactions and subsequent activation. Our results reveal that T cells can toggle between collective migration and individual random walks to prioritize either large-scale trafficking or antigen search in situ . This novel lymphoid network thus facilitates whole-body T cell trafficking and antigen surveillance in the absence of a lymph node system.

Significance Statement

In mammals, lymph nodes play a critical role in the initiation of adaptive immune responses by providing a dedicated place for T cells to scan antigen-presenting cells. Birds, reptiles, amphibians, and fish all maintain diverse repertoires of T cells but lack lymph nodes, raising questions about how adaptive immunity functions in lower jawed vertebrates. Here, we describe a novel network of lymphocytes in zebrafish that supports whole-body T cell trafficking and provides a site for antigen search, mirroring the function of mammalian lymph nodes. Within this network, T cells can prioritize large-scale trafficking or antigen scanning by toggling between two distinct modes of migration. This network provides valuable insights into the evolution of adaptive immunity.

New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning

Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-clusters could be determined based on movement and/or morphology. Differentiating between CD3- and CD20 positive cells is most challenging and long term-movement characteristics are indispensable. We propose morphological and movement-related prototypes of cell entities applying machine learning models. Finally, we define beyond CD clusters new subgroups within lymphocyte entities based on long term movement characteristics. In conclusion, we showed that the combination of 4D imaging and machine learning is able to define characteristics of lymphocytes not visible in 2D histology.

Distinct speed and direction memories of migrating dendritic cells diversify their search strategies

Migrating cells exhibit various motility patterns, resulting from different migration mechanisms, cell properties, or cell-environment interactions. The complexity of cell dynamics is reflected, e.g., in the diversity of the observed forms of velocity autocorrelation function-which has been widely served as a measure of diffusivity and spreading. By analyzing the dynamics of migrating dendritic cells in vitro, we disentangle the contributions of direction θ and speed v to the velocity autocorrelation. We find that the ability of cells to maintain their speed or direction of motion is unequal, reflected in different temporal decays of speed and direction autocorrelation functions, ACv(t)∼t-1.2 and ACθ(t)∼t-0.5, respectively. The larger power-law exponent of ACv(t) indicates that the cells lose their speed memory considerably faster than the direction memory. Using numerical simulations, we investigate the influence of ACθ and ACv as well as the direction-speed cross correlation Cθ-v on the search time of a persistent random walker to find a randomly located target in confinement. Although ACθ and Cθ-v play the major roles, we find that the speed autocorrelation ACv can be also tuned to minimize the search time. Adopting an optimal ACv can reduce the search time even up to 10% compared with uncorrelated spontaneous speeds. Our results suggest that migrating cells can improve their search efficiency, especially in crowded environments, through the directional or speed persistence or the speed-direction correlation.

Adaptive tracking algorithm for trajectory analysis of cells and layer-by-layer assessment of motility dynamics

Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.

Dietary Inclusion of Seabuckthorn (Hippophae rhamnoides) Mitigates Foodborne Enteritis in Zebrafish Through the Gut-Liver Immune Axis

To help prevent foodborne enteritis in aquaculture, several feed additives, such as herbal medicine, have been added to fish diets. Predictions of effective herb medicines for treating fish foodborne enteritis from key regulated DEGs (differentially expressed genes) in transcriptomic data can aid in the development of feed additives using the Traditional Chinese Medicine Integrated Database. Seabuckthorn has been assessed as a promising candidate for treating grass carp soybean-induced enteritis (SBMIE). In the present study, the SBMIE zebrafish model was used to assess seabuckthorn’s therapeutic or preventative effects. The results showed that intestinal and hepatic inflammation was reduced when seabuckthorn was added, either pathologically (improved intestinal villi morphology, less oil-drops) or growth-related (body fat deposition). Moreover, seabuckthorn may block the intestinal p53 signaling pathway, while activating the PPAR signaling pathway and fatty acid metabolism in the liver. 16S rRNA gene sequencing results also indicated a significant increase in OTU numbers and skewed overlapping with the fish meal group following the addition of seabuckthorn. Additionally, there were signs of altered gut microbiota taxa composition, particularly for reduced TM7, Sphingomonas, and Shigella, following the addition of seabuckthorn. Hindgut imaging of fluorescent immune cells in SBMIE larvae revealed the immune regulatory mechanisms at the cellular level. Seabuckthorn may significantly inhibit the inflammatory gathering of neutrophils, macrophages, and mature T cells, as well as cellular protrusions’ formation. On the other hand, in larvae, seabuckthorn inhibited the inflammatory aggregation of lck⁺ T cells but not immature lymphocytes, indicating that it affected intestinal adaptive immunity. Although seabuckthorn did not affect the distribution of intestinal CD4⁺ cells, the number of hepatic CD4⁺ cells were reduced in fish from the seabuckthorn supplementation group. Thus, the current data indicate that seabuckthorn may alleviate foodborne gut-liver symptoms by enhancing intestinal mucosal immunity and microbiota while simultaneously inhibiting hepatic adipose disposition, making it a potential additive for preventing fish foodborne gut-liver symptoms.

Real-time imaging of inflammation and its resolution: It's apparent because it's transparent

The ability to directly observe leukocyte behavior in vivo has dramatically expanded our understanding of the immune system. Zebrafish are particularly amenable to the high-resolution imaging of leukocytes during both homeostasis and inflammation. Due to its natural transparency, intravital imaging in zebrafish does not require any surgical manipulation. As a result, zebrafish are particularly well-suited for the long-term imaging required to observe the temporal and spatial events during the onset and resolution of inflammation. Here, we review major insights about neutrophil and macrophage function gained from real-time imaging of zebrafish. We discuss neutrophil reverse migration, the process whereby neutrophils leave sites of tissue damage and resolve local inflammation. Further, we discuss the current tools available for investigating immune function in zebrafish and how future studies that simultaneously image multiple leukocyte subsets can be used to further dissect mechanisms that regulate both the onset and resolution of inflammation.

A New Method Based on the von Mises-Fisher Distribution Shows that a Minority of Liver-Localized CD8 T Cells Display Hard-To-Detect Attraction to Plasmodium-Infected Hepatocytes

Malaria is a disease caused by Plasmodium parasites, resulting in over 200 million infections and 400,000 deaths every year. A critical step of malaria infection is when sporozoites, injected by mosquitoes, travel to the liver and form liver stages. Malaria vaccine candidates which induce large numbers of malaria-specific CD8 T cells in mice are able to eliminate all liver stages, preventing fulminant malaria. However, how CD8 T cells find all parasites in 48 h of the liver stage lifespan is not well understood. Using intravital microscopy of murine livers, we generated unique data on T cell search for malaria liver stages within a few hours after infection. To detect attraction of T cells to an infection site, we used the von Mises-Fisher distribution in 3D, similar to the 2D von Mises distribution previously used in ecology. Our results suggest that the vast majority (70-95%) of malaria-specific and non-specific liver-localized CD8 T cells did not display attraction towards the infection site, suggesting that the search for malaria liver stages occurs randomly. However, a small fraction (15-20%) displayed weak but detectable attraction towards parasites which already had been surrounded by several T cells. We found that speeds and turning angles correlated with attraction, suggesting that understanding mechanisms that determine the speed of T cell movement in the liver may improve the efficacy of future T cell-based vaccines. Stochastic simulations suggest that a small movement bias towards the parasite dramatically reduces the number of CD8 T cells needed to eliminate all malaria liver stages, but to detect such attraction by individual cells requires data from long imaging experiments which are not currently feasible. Importantly, as far as we know this is the first demonstration of how activated/memory CD8 T cells might search for the pathogen in nonlymphoid tissues a few hours after infection. We have also established a framework for how attraction of individual T cells towards a location in 3D can be rigorously evaluated.

Quantifying Human Natural Killer Cell Migration by Imaging and Image Analysis

Migration is an important function for natural killer cells. Cell motility has implications in development, tissue infiltration, and cytotoxicity, and measuring the properties of natural killer (NK) cell migration using in vitro assays can be highly informative. Many researchers have an interest in studying properties of NK cell migration in the context of genetic mutation, disease, or in specific tissues and microenvironments. Motility assays can also provide information on the localization of proteins during different phases of cell migration. These assays can be performed on different surfaces for migration or coupled with chemoattractants and/or target cells to test functional outcomes or characterize cell migration speeds and phenotypes. NK cells undergo migration during differentiation in tissue, and these conditions can be modeled by culturing NK cells on a confluent bed of stromal cells on glass and imaging cell migration. Alternatively, fibronectin- or ICAM-1-coated surfaces promote NK cell migration and can be used as substrates. Here, we will describe techniques for the experimental setup and analysis of NK cell motility assays by confocal microscopy or in-incubator imaging using commercially available systems. Finally, we describe open-source software for analyzing cell migration using manual tracking or automated approaches and discuss considerations for the implementation of each of these methods.

Orientational memory of active particles in multistate non-Markovian processes

The orientational memory of particles can serve as an effective measure of diffusivity, spreading, and search efficiency in complex stochastic processes. We develop a theoretical framework to describe the decay of directional correlations in a generic class of stochastic active processes consisting of distinct states of motion characterized by their persistence and switching probabilities between the states. For exponentially distributed sojourn times, the orientation autocorrelation is analytically derived and the characteristic times of its crossovers are obtained in terms of the persistence of each state and the switching probabilities. We show how nonexponential sojourn-time distributions of interest, such as Gaussian and power-law distributions, can result from history-dependent transitions between the states. The relaxation behavior of the correlation function in such non-Markovian processes is governed by the history dependence of the switching probabilities and cannot be solely determined by the mean sojourn times of the states.

Moving beyond velocity: Opportunities and challenges to quantify immune cell behavior

The analysis of cellular behavior using intravital multi-photon microscopy has contributed substantially to our understanding of the priming and effector phases of immune responses. Yet, many questions remain unanswered and unexplored. Though advancements in intravital imaging techniques and animal models continue to drive new discoveries, continued improvements in analysis methods are needed to extract detailed information about cellular behavior. Focusing on dendritic cell (DC) and T cell interactions as an exemplar, here we discuss key limitations for intravital imaging studies and review and explore alternative approaches to quantify immune cell behavior. We touch upon current developments in deep learning models, as well as established methods from unrelated fields such as ecology to detect and track objects over time. As developments in open-source software make it possible to process and interactively view larger datasets, the challenge for the field will be to determine how best to combine intravital imaging with multi-parameter imaging of larger tissue regions to discover new facets of leukocyte dynamics and how these contribute to immune responses.

Transendothelial migration induces differential migration dynamics of leukocytes in tissue matrix

Leukocyte extravasation into inflamed tissue is a complex process that is difficult to capture as a whole in vitro. We employed a blood-vessel-on-a-chip model in which human endothelial cells were cultured in a tube-like lumen in a collagen-1 matrix. The vessels are leak tight, creating a barrier for molecules and leukocytes. Addition of inflammatory cytokine TNF-α (also known as TNF) caused vasoconstriction, actin remodelling and upregulation of ICAM-1. Introducing leukocytes into the vessels allowed real-time visualization of all different steps of the leukocyte transmigration cascade, including migration into the extracellular matrix. Individual cell tracking over time distinguished striking differences in migratory behaviour between T-cells and neutrophils. Neutrophils cross the endothelial layer more efficiently than T-cells, but, upon entering the matrix, neutrophils display high speed but low persistence, whereas T-cells migrate with low speed and rather linear migration. In conclusion, 3D imaging in real time of leukocyte extravasation in a vessel-on-a-chip enables detailed qualitative and quantitative analysis of different stages of the full leukocyte extravasation process in a single assay. This article has an associated First Person interview with the first authors of the paper.

Local actin dynamics couple speed and persistence in a cellular Potts model of cell migration

Cell migration is astoundingly diverse. Molecular signatures, cell-cell interactions, and environmental structures each play their part in shaping cell motion, yielding numerous morphologies and migration modes. Nevertheless, in recent years, a simple unifying law was found to describe cell migration across many different cell types and contexts: faster cells turn less frequently. This universal coupling between speed and persistence (UCSP) was explained by retrograde actin flow from front to back, but it remains unclear how this mechanism generalizes to cells with complex shapes and cells migrating in structured environments, which may not have a well-defined front-to-back orientation. Here, we present an in-depth characterization of an existing cellular Potts model, in which cells polarize dynamically from a combination of local actin dynamics (stimulating protrusions) and global membrane tension along the perimeter (inhibiting protrusions). We first show that the UCSP emerges spontaneously in this model through a cross talk of intracellular mechanisms, cell shape, and environmental constraints, resembling the dynamic nature of cell migration in vivo. Importantly, we find that local protrusion dynamics suffice to reproduce the UCSP-even in cases in which no clear global, front-to-back polarity exists. We then harness the spatial nature of the cellular Potts model to show how cell shape dynamics limit both the speed and persistence a cell can reach and how a rigid environment such as the skin can restrict cell motility even further. Our results broaden the range of potential mechanisms underlying the speed-persistence coupling that has emerged as a fundamental property of migrating cells.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Single-Domain Multiferroic Array-Addressable Terfenol-D (SMArT) Micromagnets for Programmable Single-Cell Capture and Release

Programming magnetic fields with microscale control can enable automation at the scale of single cells ≈10 µm. Most magnetic materials provide a consistent magnetic field over time but the direction or field strength at the microscale is not easily modulated. However, magnetostrictive materials, when coupled with ferroelectric material (i.e., strain-mediated multiferroics), can undergo magnetization reorientation due to voltage-induced strain, promising refined control of magnetization at the micrometer-scale. This work demonstrates the largest single-domain microstructures (20 µm) of Terfenol-D (Tb_0.3 Dy_0.7 Fe_1.92 ), a material that has the highest magnetostrictive strain of any known soft magnetoelastic material. These Terfenol-D microstructures enable controlled localization of magnetic beads with sub-micrometer precision. Magnetically labeled cells are captured by the field gradients generated from the single-domain microstructures without an external magnetic field. The magnetic state on these microstructures is switched through voltage-induced strain, as a result of the strain-mediated converse magnetoelectric effect, to release individual cells using a multiferroic approach. These electronically addressable micromagnets pave the way for parallelized multiferroics-based single-cell sorting under digital control for biotechnology applications.

Persistence-Speed Coupling Enhances the Search Efficiency of Migrating Immune Cells

Migration of immune cells within the human body allows them to fulfill their main function of detecting pathogens. We present experimental evidence showing the optimality of the search strategy of these cells, which is of crucial importance to achieve an efficient immune response. We find that the speed and directional persistence of migrating dendritic cells in our in vitro experiments are highly correlated, which enables them to reduce their search time. We introduce theoretically a new class of random search optimization problems by minimizing the mean first-passage time (MFPT) with respect to the strength of the coupling between influential parameters. We derive an analytical expression for the MFPT in a confined geometry and verify that the correlated motion enhances the search efficiency if the mean persistence length is sufficiently shorter than the confinement size. Our correlated search optimization approach provides an efficient searching recipe and predictive power in a broad range of correlated stochastic processes.