CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems

Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals

Cellular function is controlled through intricate networks of signals, which lead to the myriad pathways governing cell fate. Fluorescent biosensors have enabled the study of these signaling pathways in living systems across temporal and spatial scales. Over the years there has been an explosion in the number of fluorescent biosensors, as they have become available for numerous targets, utilized across spectral space, and suited for various imaging techniques. To guide users through this extensive biosensor landscape, we discuss critical aspects of fluorescent proteins for consideration in biosensor development, smart tagging strategies, and the historical and recent biosensors of various types, grouped by target, and with a focus on the design and recent applications of these sensors in living systems.

Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach

Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study, we addressed this gap by combining a specialized surface to enhance cell attachment, the FUCCI(CA)2 sensor, an automated image analysis pipeline, and a custom machine learning algorithm. This approach enabled precise measurement of cell cycle phase durations in non-adherent cells. This method was validated in acute myeloid leukemia cell lines NB4 and Kasumi-1, which have unique cell cycle characteristics, and we tested the impact of cell cycle-modulating drugs on NB4 cells. Our cell cycle analysis system, which is also compatible with adherent cells, is fully automated and freely available, providing detailed insights from hundreds of cells under various conditions. This report presents a valuable tool for advancing cancer research and drug development by enabling comprehensive, automated cell cycle analysis in both adherent and non-adherent cells.

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells

Understanding mechanisms underlying various physiological and pathological processes often requires accurate and fully automated analysis of dense cell populations that collectively migrate. In such multicellular systems, there is a rising interest in the relations between biophysical and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework that is designed, unlike previous tools, for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. We integrated into ConfluentFUCCI's pipeline state-of-the-art tools such as Cellpose, TrackMate, and Napari, some of which incorporate deep learning, and we wrap the entire tool into an isolated computational environment termed container. This provides an easy installation and workflow that is independent of any specific operation system. ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

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.

Automated tracking of cell migration in phase contrast images with CellTraxx

The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells' ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.

Initial refinement of data from video-based single-cell tracking

Background

Video recording of cells offers a straightforward way to gain valuable information from their response to treatments. An indispensable step in obtaining such information involves tracking individual cells from the recorded data. A subsequent step is reducing such data to represent essential biological information. This can help to compare various single-cell tracking data yielding a novel source of information. The vast array of potential data sources highlights the significance of methodologies prioritizing simplicity, robustness, transparency, affordability, sensor independence, and freedom from reliance on specific software or online services.

Methods

The provided data presents single-cell tracking of clonal (A549) cells as they grow in two-dimensional (2D) monolayers over 94 hours, spanning several cell cycles. The cells are exposed to three different concentrations of yessotoxin (YTX). The data treatments showcase the parametrization of population growth curves, as well as other statistical descriptions. These include the temporal development of cell speed in family trees with and without cell death, correlations between sister cells, single-cell average displacements, and the study of clustering tendencies.

Results

Various statistics obtained from single-cell tracking reveal patterns suitable for data compression and parametrization. These statistics encompass essential aspects such as cell division, movements, and mutual information between sister cells.

Conclusion

This work presents practical examples that highlight the abundant potential information within large sets of single-cell tracking data. Data reduction is crucial in the process of acquiring such information which can be relevant for phenotypic drug discovery and therapeutics, extending beyond standardized procedures. Conducting meaningful big data analysis typically necessitates a substantial amount of data, which can stem from standalone case studies as an initial foundation.

FUCCItrack: An all-in-one software for single cell tracking and cell cycle analysis

Beyond the more conventional single-cell segmentation and tracking, single-cell cycle dynamics is gaining a growing interest in the field of cell biology. Thanks to sophisticated systems, such as the fluorescent ubiquitination-based cell cycle indicator (FUCCI), it is now possible to study cell proliferation, migration, changes in nuclear morphology and single cell cycle dynamics, quantitatively and in real time. In this work, we introduce FUCCItrack, an all-in-one, semi-automated software to segment, track and visualize FUCCI modified cell lines. A user-friendly complete graphical user interface is presented to record and quantitatively analyze both collective cell proliferation as well as single cell information, including migration and changes in nuclear or cell morphology as a function of cell cycle. To enable full control over the analysis, FUCCItrack also contains features for identification of errors and manual corrections.