An end-to-end software solution for the analysis of high-throughput single-cell migration data

Methods and computational tools to study eukaryotic cell migration in vitro

Cellular movement is essential for many vital biological functions where it plays a pivotal role both at the single cell level, such as during division or differentiation, and at the macroscopic level within tissues, where coordinated migration is crucial for proper morphogenesis. It also has an impact on various pathological processes, one for all, cancer spreading. Cell migration is a complex phenomenon and diverse experimental methods have been developed aimed at dissecting and analysing its distinct facets independently. In parallel, corresponding analytical procedures and tools have been devised to gain deep insight and interpret experimental results. Here we review established experimental techniques designed to investigate specific aspects of cell migration and present a broad collection of historical as well as cutting-edge computational tools used in quantitative analysis of cell motion.

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks

MOTIVATION

Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics.

RESULTS

We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications.

AVAILABILITY AND IMPLEMENTATION

MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.

3D printed inserts for reproducible high throughput screening of cell migration

Cell migration is a fundamental and complex phenomenon that occurs in normal physiology and in diseases like cancer. Hence, understanding cell migration is very important in the fields of developmental biology and biomedical sciences. Cell migration occurs in 3 dimensions (3D) and involves an interplay of migrating cell(s), neighboring cells, extracellular matrix, and signaling molecules. To understand this phenomenon, most of the currently available techniques still rely on 2-dimensional (2D) cell migration assay, also known as the scratch assay or the wound healing assay. These methods suffer from limited reproducibility in creating a cell-free region (a scratch or a wound). Mechanical/heat related stress to cells is another issue which hampers the applicability of these methods. To tackle these problems, we developed an alternative method based on 3D printed biocompatible cell inserts, for quantifying cell migration in 24-well plates. The inserts were successfully validated via a high throughput assay for following migration of lung cancer cell line (A549 cell line) in the presence of standard cell migration promoters and inhibitors. We also developed an accompanying image analysis pipeline which demonstrated that our method outperforms the state-of-the-art methodologies for assessing the cell migration in terms of reproducibility and simplicity.

Morphomigrational description as a new approach connecting cell's migration with its morphology

The examination of morphology and migration of cells plays substantial role in understanding the cellular behaviour, being described by plethora of quantitative parameters and models. These descriptions, however, treat cell migration and morphology as independent properties of temporal cell state, while not taking into account their strong interdependence in adherent cells. Here we present the new and simple mathematical parameter called signed morphomigrational angle (sMM angle) that links cell geometry with translocation of cell centroid, considering them as one morphomigrational behaviour. The sMM angle combined with pre-existing quantitative parameters enabled us to build a new tool called morphomigrational description, used to assign the numerical values to several cellular behaviours. Thus, the cellular activities that until now were characterized using verbal description or by complex mathematical models, are described here by a set of numbers. Our tool can be further used in automatic analysis of cell populations as well as in studies focused on cellular response to environmental directional signals.

Quantification of cell migration: metrics selection to model application

Cell migration plays an essential role in physiological and pathological states, such as immune response, tissue generation and tumor development. This phenomenon can occur spontaneously or it can be triggered by an external stimuli, including biochemical, mechanical, or electrical cues that induce or direct cells to migrate. The migratory response to these cues is foundational to several fields including neuroscience, cancer and regenerative medicine. Various platforms are available to qualitatively and quantitatively measure cell migration, making the measurements of cell motility straight-forward. Migratory behavior must be analyzed by multiple metrics and then models to connect the measurements to physiological meaning. This review will focus on describing and quantifying cell movement for individual cell migration.

MACC1-induced migration in tumors: Current state and perspective

Malignant tumors are still a global, heavy health burden. Many tumor types cannot be treated curatively, underlining the need for new treatment targets. In recent years, metastasis associated in colon cancer 1 (MACC1) was identified as a promising biomarker and drug target, as it is promoting tumor migration, initiation, proliferation, and others in a multitude of solid cancers. Here, we will summarize the current knowledge about MACC1-induced tumor cell migration with a special focus on the cytoskeletal and adhesive systems. In addition, a brief overview of several in vitro models used for the analysis of cell migration is given. In this context, we will point to issues with the currently most prevalent models used to study MACC1-dependent migration. Lastly, open questions about MACC1-dependent effects on tumor cell migration will be addressed.

A three component model for superdiffusive motion effectively describes migration of eukaryotic cells moving freely or under a directional stimulus

Although the simple diffusion model can effectively describe the movement of eukaryotic cells on a culture surface observed at relatively low sampling frequency, at higher sampling rates more complex models are often necessary to better fit the experimental data. Currently available models can describe motion paths by involving additional parameters, such as linearity or directional persistence in time. However sometimes difficulties arise as it is not easy to effectively evaluate persistence in presence of a directional bias. Here we present a procedure which helps solve this problem, based on a model which describes displacement as the vectorial sum of three components: diffusion, persistence and directional bias. The described model has been tested by analysing the migratory behaviour of simulated cell populations and used to analyse a collection of experimental datasets, obtained by observing cell cultures in time lapse microscopy. Overall, the method produces a good description of migration behaviour as it appears to capture the expected increase in the directional bias in presence of wound without a large concomitant increase in the persistence module, allowing it to remain as a physically meaningful quantity in the presence of a directional stimulus.

MigraR: An open-source, R-based application for analysis and quantification of cell migration parameters

BACKGROUND AND OBJECTIVE

Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics.

METHODS

This report describes MigraR, an intuitive graphical user interface executed from the RStudio^TM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness.

RESULTS

MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software.

CONCLUSIONS

Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.

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.

Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex

BACKGROUND

Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation.

RESULTS

We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery.

CONCLUSIONS

The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.

Enhanced segmentation of label-free cells for automated migration and interaction tracking

In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time-lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well-known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label-free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell-cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.

Perivascular macrophages create an intravascular niche for CD8+ T cell localisation prior to the onset of fatal experimental cerebral malaria

Objectives

The immunologic events that build up to the fatal neurological stage of experimental cerebral malaria (ECM) are incompletely understood. Here, we dissect immune cell behaviour occurring in the central nervous system (CNS) when Plasmodium berghei ANKA (PbA)‐infected mice show only minor clinical signs.

Methods

A 2‐photon intravital microscopy (2P‐IVM) brain imaging model was used to study the spatiotemporal context of early immunological events in situ during ECM.

Results

Early in the disease course, antigen‐specific CD8⁺ T cells came in contact and arrested on the endothelium of post‐capillary venules. CD8⁺ T cells typically adhered adjacent to, or were in the near vicinity of, perivascular macrophages (PVMs) that line post‐capillary venules. Closer examination revealed that CD8⁺ T cells crawled along the inner vessel wall towards PVMs that lay on the abluminal side of large post‐capillary venules. ‘Activity hotspots’ in large post‐capillary venules were characterised by T‐cell localisation, activated morphology and clustering of PVM, increased abutting of post‐capillary venules by PVM and augmented monocyte accumulation. In the later stages of infection, when mice exhibited neurological signs, intravascular CD8⁺ T cells increased in number and changed their behaviour, actively crawling along the endothelium and displaying frequent, short‐term interactions with the inner vessel wall at hotspots.

Conclusion

Our study suggests an active interaction between PVM and CD8⁺ T cells occurs across the blood–brain barrier (BBB) in early ECM, which may be the initiating event in the inflammatory cascade leading to BBB alteration and neuropathology.

A least microenvironmental uncertainty principle (LEUP) as a generative model of collective cell migration mechanisms

Collective migration is commonly observed in groups of migrating cells, in the form of swarms or aggregates. Mechanistic models have proven very useful in understanding collective cell migration. Such models, either explicitly consider the forces involved in the interaction and movement of individuals or phenomenologically define rules which mimic the observed behavior of cells. However, mechanisms leading to collective migration are varied and specific to the type of cells involved. Additionally, the precise and complete dynamics of many important chemomechanical factors influencing cell movement, from signalling pathways to substrate sensing, are typically either too complex or largely unknown. The question is how to make quantitative/qualitative predictions of collective behavior without exact mechanistic knowledge. Here we propose the least microenvironmental uncertainty principle (LEUP) that may serve as a generative model of collective migration without precise incorporation of full mechanistic details. Using statistical physics tools, we show that the famous Vicsek model is a special case of LEUP. Finally, to test the biological applicability of our theory, we apply LEUP to construct a model of the collective behavior of spherical Serratia marcescens bacteria, where the underlying migration mechanisms remain elusive.

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

Thymosin β4 Regulates Focal Adhesion Formation in Human Melanoma Cells and Affects Their Migration and Invasion

Thymosin β4 (Tβ4), a multifunctional 44-amino acid polypeptide and a member of actin-binding proteins (ABPs), plays an important role in developmental processes and wound healing. In recent years an increasing number of data has been published suggesting Tβ4's involvement in tumorigenesis. However, Tβ4's role in melanoma tumor development still remains to be elucidated. In our study we demonstrate that Tβ4 is crucial for melanoma adhesion and invasion. For the purpose of our research we tested melanoma cell lines differing in invasive potential. Moreover, we applied shRNAs to silence TMSB4X (gene encoding Tβ4) expression in a cell line with high TMSB4X expression. We found out that Tβ4 is not only a component of focal adhesions (FAs) and interacts with several FAs components but also regulates FAs formation. We demonstrate that Tβ4 level has an impact on FAs' number and morphology. Moreover, manipulation with TMSB4X expression resulted in changes in cells' motility on non-coated and Matrigel^TM (resembling basement membrane composition)-coated surfaces and drastically decreased invasion abilities of the cells. Additionally, a correlation between Tβ4 expression level and exhibition of mesenchymal-like [epithelial-mesenchymal transition (EMT)] features was discovered. Cells with lowered TMSB4X expression were less EMT-progressed than control cells. Summarizing, obtained results show that Tβ4 by regulating melanoma cells' adhesion has an impact on motility features and EMT. Our study not only contributes to a better understanding of the processes underlying melanoma cells' capacity to create metastases but also highlights Tβ4 as a potential target for melanoma management therapy.

Modeling and analysis of melanoblast motion

Melanoblast migration is important for embryogenesis and is a key feature of melanoma metastasis. Many studies have characterized melanoblast movement, focusing on statistical properties and have highlighted basic mechanisms of melanoblast motility. We took a slightly different and complementary approach: we previously developed a mathematical model of melanoblast motion that enables the testing of biological assumptions about the displacement of melanoblasts and we created tests to analyze the geometric features of cell trajectories and the specific issue of trajectory interactions. Within this model, we performed simulations and compared the results with experimental data using geometric tests. In this paper, we developed the associated mathematical model and the main focus is to study the crossings between trajectories with new theoretical results about the variation of number of intersection points with respect to the crossing times. Using these results it is possible to study the random nature of displacements and the interactions between trajectories. This analysis has raised new questions, leading to the generation of strong arguments in favor of a trail left behind each moving melanoblast.

DRfit: a Java tool for the analysis of discrete data from multi-well plate assays

Background

Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training.

Results

We have developed an easy-to-use, interactive software tool for the analysis of multiple data sets comprising replicates of discrete bivariate data points. For each dataset, the software identifies the replicate data points from a defined matrix layout and calculates their means and standard errors. The averaged values are then automatically fitted using either a linear or a logistic dose response function.

Conclusions

DRfit is a practical and convenient tool for the analysis of one or multiple sets of discrete data points acquired as replicates from multi-well plate assays. The design of the graphical user interface and the built-in analysis features make it a flexible and useful tool for a wide range of different assays.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2891-5) contains supplementary material, which is available to authorized users.

Interplay Between the Persistent Random Walk and the Contact Inhibition of Locomotion Leads to Collective Cell Behaviors

Cell migration plays an important role in physiology and pathophysiology. It was observed in the experiments that cells, such as fibroblast, leukocytes, and cancer cells, exhibit a wide variety of migratory behaviors, such as persistent random walk, contact inhibition of locomotion, and ordered behaviors. To identify biophysical mechanisms for these cellular behaviors, we developed a rigorous computational model of cell migration on a two-dimensional non-deformable substrate. Cells in the model undergo motion driven by mechanical interactions between cellular protrusions and the substrate via the balance of tensile forces. Properties of dynamic formation of lamellipodia induced the persistent random walk behavior of a migrating cell. When multiple cells are included in the simulation, the model recapitulated the contact inhibition of locomotion between cells at low density without any phenomenological assumptions or momentum transfer. Instead, the model showed that contact inhibition of locomotion can emerge via indirect interactions between the cells through their interactions with the underlying substrate. At high density, contact inhibition of locomotion between numerous cells gave rise to confined motions or ordered behaviors, depending on cell density and how likely lamellipodia turn over due to contact with other cells. Results in our study suggest that various collective migratory behaviors may emerge without more restrictive assumptions or direct cell-to-cell biomechanical interactions.

DoRes within CellMissy: dose-response analysis on cell migration and related data

Summary

In cancer research, cell-based assays are used to assess cell migration and invasion. The major bottleneck is the lack of automated tools to visualize and analyse the large amounts of biological dose-response data produced. To address this challenge, we have developed an automated and free software package for dose-response analyses, DoRes, which is released as an add-on of the freely available and open-source tool CellMissy, dedicated to the management and analysis of cell migration data. DoRes implements non-linear curve fitting functionality into a robust, user-friendly and flexible software package with the possibility of importing a tabular file or starting from a cell migration experiment. We demonstrate the ability of the software by analysing public dose-response data and a typical cell migration experiment, and show that the extracted dose-response parameters and the calculated statistical values are consistently comparable to those of the widely used, commercial software GraphPad Prism.

Availability and implementation

The software here presented is a new module in CellMissy, an open-source and cross-platform package dedicated to the management, storage and analysis of cell migration data. The new module is written in Java, and inherits the cross-platform support from CellMissy. Source code and binaries are freely available under the Apache2 open-source licence at https://github.com/compomics/cellmissy/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of Invasion Dynamics of Matrix-Embedded Cells in a Multisample Format

In vitro tests of cancer cell invasion are the "first line" tools of preclinical researchers for screening the multitude of chemical compounds or cell perturbations that may aid in halting or treating cancer malignancy. In order to have predictive value or to contribute to designing personalized treatment regimes, these tests need to take into account the cancer cell environment and measure effects on invasion in sufficient detail. The in vitro invasion assays presented here are a trade-off between feasibility in a multisample format and mimicking the complexity of the tumor microenvironment. They allow testing multiple samples and conditions in parallel using 3D-matrix-embedded cells and deal with the heterogeneous behavior of an invading cell population in time. We describe the steps to take, the technical problems to tackle and useful software tools for the entire workflow: from the experimental setup to the quantification of the invasive capacity of the cells. The protocol is intended to guide researchers to standardize experimental set-ups and to annotate their invasion experiments in sufficient detail. In addition, it provides options for image processing and a solution for storage, visualization, quantitative analysis, and multisample comparison of acquired cell invasion data.