Probing cell proliferation: Considerations for dye selection.

Broadly speaking, cell tracking dyes are fluorescent compounds that bind stably to components on or within the cells so the fate of the labeled cells can be followed. Their staining should be bright and homogeneous without affecting cell function. For purposes of monitoring cell proliferation, each time a cell divides the intensity of cell tracking dye should diminish equally between daughter cells. These dyes can be grouped into two different classes. Protein reactive dyes label cells by reacting covalently but non-selectively with intracellular proteins. Carboxyfluorescein diacetate succinimidyl ester (CFSE) is the prototypic general protein label. Membrane intercalating dyes label cells by partitioning non-selectively and non-covalently within the plasma membrane. The PKH membrane dyes are examples of lipophilic compounds whose chemistry allows for their retention within biological membranes without affecting cellular growth, viability, or proliferation when used properly. Here we provide considerations based for labeling cell lines and peripheral blood mononuclear cells using both classes of dyes. Examples from optimization experiments are presented along with critical aspects of the staining procedures to help mitigate common risks. Of note, we present data where a logarithmically growing cell line is labeled with both a protein dye and a membrane tracking dye to compare dye loss rates over 6days. We found that dual stained cells paralleled dye loss of the corresponding single stained cells. The decrease in fluorescence intensity by protein reactive dyes, however, was more rapid than that with the membrane reactive dyes, indicating the presence of additional division-independent dye loss.

Label-free metabolic optical biomarkers track stem cell fate transition in real time.

Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes.

SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations.

Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalized algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multiclass cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications.

HFM-Tracker: a cell tracking algorithm based on hybrid feature matching.

Cell migration is known to be a fundamental biological process, playing an essential role in development, homeostasis, and diseases. This paper introduces a cell tracking algorithm named HFM-Tracker (Hybrid Feature Matching Tracker) that automatically identifies cell migration behaviours in consecutive images. It combines Contour Attention (CA) and Adaptive Confusion Matrix (ACM) modules to accurately capture cell contours in each image and track the dynamic behaviors of migrating cells in the field of view. Cells are firstly located and identified via the CA module-based cell detection network, and then associated and tracked via a cell tracking algorithm employing a hybrid feature-matching strategy. This proposed HFM-Tracker exhibits superiorities in cell detection and tracking, achieving 75% in MOTA (Multiple Object Tracking Accuracy) and 65% in IDF1 (ID F1 score). It provides quantitative analysis of the cell morphology and migration features, which could further help in understanding the complicated and diverse cell migration processes.

Overcoming Xenoantigen Immunity to Enable Cellular Tracking and Gene Regulation with Immune-competent "NoGlow" Mice.

The ability to temporally regulate gene expression and track labeled cells makes animal models powerful biomedical tools. However, sudden expression of xenobiotic genes [e.g., GFP, luciferase (Luc), or rtTA3] can trigger inadvertent immunity that suppresses foreign protein expression or results in complete rejection of transplanted cells. Germline exposure to foreign antigens somewhat addresses these challenges; however, native fluorescence and bioluminescence abrogates the utility of reporter proteins and highly spatiotemporally restricted expression can lead to suboptimal xenoantigen tolerance. To overcome these unwanted immune responses and enable reliable cell tracking/gene regulation, we developed a novel mouse model that selectively expresses antigen-intact but nonfunctional forms of GFP and Luc, as well as rtTA3, after CRE-mediated recombination. Using tissue-specific CREs, we observed model and sex-based differences in immune tolerance to the encoded xenoantigens, illustrating the obstacles of tolerizing animals to foreign genes and validating the utility of these "NoGlow" mice to dissect mechanisms of central and peripheral tolerance. Critically, tissue unrestricted NoGlow mice possess no detectable background fluorescence or luminescence and exhibit limited adaptive immunity against encoded transgenic xenoantigens after vaccination. Moreover, we demonstrate that NoGlow mice allow tracking and tetracycline-inducible gene regulation of triple-transgenic cells expressing GFP/Luc/rtTA3, in contrast to transgene-negative immune-competent mice that eliminate these cells or prohibit metastatic seeding. Notably, this model enables de novo metastasis from orthotopically implanted, triple-transgenic tumor cells, despite high xenoantigen expression. Altogether, the NoGlow model provides a critical resource for in vivo studies across disciplines, including oncology, developmental biology, infectious disease, autoimmunity, and transplantation. SIGNIFICANCE: Multitolerant NoGlow mice enable tracking and gene manipulation of transplanted tumor cells without immune-mediated rejection, thus providing a platform to investigate novel mechanisms of adaptive immunity related to metastasis, immunotherapy, and tolerance.

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.

Monitoring Cell Proliferation by Dye Dilution: Considerations for Panel Design.

High dimensional studies that include proliferation dyes face two inherent challenges in panel design. First, the more rounds of cell division to be monitored based on dye dilution, the greater the starting intensity of the labeled parent cells must be in order to distinguish highly divided daughter cells from background autofluorescence. Second, the greater their starting intensity, the more difficult it becomes to avoid spillover of proliferation dye signal into adjacent spectral channels, with resulting limitations on the use of other fluorochromes and ability to resolve dim signals of interest. In the third and fourth editions of this series, we described the similarities and differences between protein-reactive and membrane-intercalating dyes used for general cell tracking, provided detailed protocols for optimized labeling with each dye type, and summarized characteristics to be tested by the supplier and/or user when validating either dye type for use as a proliferation dye. In this fifth edition, we review: (a) Fundamental assumptions and critical controls for dye dilution proliferation assays; (b) Methods to evaluate the effect of labeling on cell growth rate and test the fidelity with which dye dilution reports cell division; and. (c) Factors that determine how many daughter generations can be accurately included in proliferation modeling. We also provide an expanded section on spectral characterization, using data collected for three protein-reactive dyes (CellTrace™ Violet, CellTrace™ CFSE, and CellTrace™ Far Red) and three membrane-intercalating dyes (PKH67, PKH26, and CellVue® Claret) on three different cytometers to illustrate typical decisions and trade-offs required during multicolor panel design. Lastly, we include methods and controls for assessing regulatory T cell potency, a functional assay that incorporates the "know your dye" and "know your cytometer" principles described herein.

MRI-Based Cell Tracking of OATP-Expressing Cell Transplants by Pre-Labeling with Gd-EOB-DTPA.

PURPOSE: A critical step in cell-based therapies is determining the exact position of transplanted cells immediately post-transplant. Here, we devised a method to detect cell transplants immediately post-transplant, using a clinical gadolinium-based contrast agent. These cells were detected as hyperintense signals using a clinically familiar T1-weighted MRI protocol. PROCEDURES: HEK293 cells were stably transduced to express human OATP1B3, a hepatic organic anion transporting polypeptide that transports Gd-EOB-DTPA into cells that express the transporters, the intracellular accumulation of which cells causes signal enhancement on T1-weighted MRI. Cells were pre-labeled prior to injection in media containing Gd-EOB-DTPA for MRI evaluation and indocyanine green for cryofluorescence tomography validation. Labeled cells were injected into chicken hearts, in vitro, after which MRI and cryofluorescence tomography were performed in sequence. RESULTS: OATP1B3-expressing cells had substantially reduced T1 following labeling with Gd-EOB-DTPA in culture. Following their implantation into chicken heart, these cells were robustly identified in T1-weighted MRI, with image-derived injection volumes of cells commensurate with intended injection volumes. Cryofluorescence tomography showed that the areas of signal enhancement in MRI overlapped with areas of indocyanine green signal, indicating that MRI signal enhancement was due to the transplanted cells. CONCLUSIONS: OATP1B3-expressing cells can be pre-labeled with Gd-EOB-DTPA prior to injection into tissue, affording the use of clinically familiar T1-weighted MRI to robustly detect cell transplants immediately after transplant. This procedure is easily generalizable and has potential advantages over the use of iron oxide based cell labeling agents and imaging procedures.

Phototruncation cell tracking with near-infrared photoimmunotherapy using heptamethine cyanine dye to visualise migratory dynamics of immune cells.

BACKGROUND: Noninvasive in vivo cell tracking is valuable in understanding the mechanisms that enhance anti-cancer immunity. We have recently developed a new method called phototruncation-assisted cell tracking (PACT), that uses photoconvertible cell tracking technology to detect in vivo cell migration. This method has the advantages of not requiring genetic engineering of cells and employing tissue-penetrant near-infrared light. METHODS: We applied PACT to monitor the migration of immune cells between a tumour and its tumour-draining lymph node (TDLN) after near-infrared photoimmunotherapy (NIR-PIT). FINDINGS: PACT showed a significant increase in the migration of dendritic cells (DCs) and macrophages from the tumour to the TDLN immediately after NIR-PIT. This migration by NIR-PIT was abrogated by inhibiting the sphingosine-1-phosphate pathway or Gαi signaling. These results were corroborated by intranodal immune cell profiles at two days post-treatment; NIR-PIT significantly induced DC maturation and increased and activated the CD8+ T cell population in the TDLN. Furthermore, PACT revealed that NIR-PIT significantly enhanced the migration of CD8+ T cells from the TDLN to the tumour four days post-treatment, which was consistent with the immunohistochemical assessment of tumour-infiltrating lymphocytes and tumour regression. INTERPRETATION: Immune cells dramatically migrated between the tumour and TDLN following NIR-PIT, indicating its potential as an immune-stimulating therapy. Also, PACT is potentially applicable to a wide range of immunological research. FUNDING: This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Centre for Cancer Research (grant number: ZIA BC011513 and ZIA BC011506).

Self-enhanced mobility enables vortex pattern formation in living matter.

Ranging from subcellular organelle biogenesis to embryo development, the formation of self-organized structures is a hallmark of living systems. Whereas the emergence of ordered spatial patterns in biology is often driven by intricate chemical signalling that coordinates cellular behaviour and differentiation1-4, purely physical interactions can drive the formation of regular biological patterns such as crystalline vortex arrays in suspensions of spermatozoa5 and bacteria6. Here we discovered a new route to self-organized pattern formation driven by physical interactions, which creates large-scale regular spatial structures with multiscale ordering. Specifically we found that dense bacterial living matter spontaneously developed a lattice of mesoscale, fast-spinning vortices; these vortices each consisted of around 104-105 motile bacterial cells and were arranged in space at greater than centimetre scale and with apparent hexagonal order, whereas individual cells in the vortices moved in coordinated directions with strong polar and vortical order. Single-cell tracking and numerical simulations suggest that the phenomenon is enabled by self-enhanced mobility in the system-that is, the speed of individual cells increasing with cell-generated collective stresses at a given cell density. Stress-induced mobility enhancement and fluidization is prevalent in dense living matter at various scales of length7-9. Our findings demonstrate that self-enhanced mobility offers a simple physical mechanism for pattern formation in living systems and, more generally, in other active matter systems10 near the boundary of fluid- and solid-like behaviours11-17.

Single-cell tracking as a tool for studying EMT-phenotypes.

This article demonstrates that label-free single-cell video tracking is a useful approach for in vitro studies of Epithelial-Mesenchymal Transition (EMT). EMT is a highly heterogeneous process, involved in wound healing, embryogenesis and cancer. The process promotes metastasis, and increased understanding can aid development of novel therapeutic strategies. The role of EMT-associated biomarkers depends on biological context, making it challenging to compare and interpret data from different studies. We demonstrate single-cell video tracking for comprehensive phenotype analysis. In this study we performed single-cell video tracking on 72-h long recordings. We quantified several behaviours at a single-cell level during induced EMT in MDA-MB-468 cells. This revealed notable variations in migration speed, with different dose-response patterns and varying distributions of speed. By registering cell morphologies during the recording, we determined preferred paths of morphological transitions. We also found a clear association between migration speed and cell morphology. We found elevated rates of cell death, diminished proliferation, and an increase in mitotic failures followed by re-fusion of sister-cells. The method allows tracking of phenotypes in cell lineages, which can be particularly useful in epigenetic studies. Sister-cells were found to have significant similarities in their speeds and morphologies, illustrating the heritability of these traits.

Câncer do colo do útero: novo método de rastreamento estuda ser incorporado ao SUS

Após parecer preliminar favorável da Comissão Nacional de Incorporação de Tecnologias no Sistema Único de Saúde (Conitec) à incorporação da testagem molecular como meio de detecção do papilomavírus humano (HPV) para rastreamento do câncer do colo do útero no SUS, o Ministério da Saúde (MS) abriu uma consulta pública à sociedade civil. As contribuições foram aceitas até o dia 17 de janeiro de 2024.

4D Light-sheet Imaging of Zebrafish Cardiac Contraction.

Zebrafish is an intriguing model organism known for its remarkable cardiac regeneration capacity. Studying the contracting heart in vivo is essential for gaining insights into structural and functional changes in response to injuries. However, obtaining high-resolution and high-speed 4-dimensional (4D, 3D spatial + 1D temporal) images of the zebrafish heart to assess cardiac architecture and contractility remains challenging. In this context, an in-house light-sheet microscope (LSM) and customized computational analysis are used to overcome these technical limitations. This strategy, involving LSM system construction, retrospective synchronization, single cell tracking, and user-directed analysis, enables one to investigate the micro-structure and contractile function across the entire heart at the single-cell resolution in the transgenic Tg(myl7:nucGFP) zebrafish larvae. Additionally, we are able to further incorporate microinjection of small molecule compounds to induce cardiac injury in a precise and controlled manner. Overall, this framework allows one to track physiological and pathophysiological changes, as well as the regional mechanics at the single-cell level during cardiac morphogenesis and regeneration.

Counter-on-chip for bacterial cell quantification, growth, and live-dead estimations.

Quantifying bacterial cell numbers is crucial for experimental assessment and reproducibility, but the current technologies have limitations. The commonly used colony forming units (CFU) method causes a time delay in determining the actual numbers. Manual microscope counts are often error-prone for submicron bacteria. Automated systems are costly, require specialized knowledge, and are erroneous when counting smaller bacteria. In this study, we took a different approach by constructing three sequential generations (G1, G2, and G3) of counter-on-chip that accurately and timely count small particles and/or bacterial cells. We employed 2-photon polymerization (2PP) fabrication technology; and optimized the printing and molding process to produce high-quality, reproducible, accurate, and efficient counters. Our straightforward and refined methodology has shown itself to be highly effective in fabricating structures, allowing for the rapid construction of polydimethylsiloxane (PDMS)-based microfluidic devices. The G1 comprises three counting chambers with a depth of 20 µm, which showed accurate counting of 1 µm and 5 µm microbeads. G2 and G3 have eight counting chambers with depths of 20 µm and 5 µm, respectively, and can quickly and precisely count Escherichia coli cells. These systems are reusable, accurate, and easy to use (compared to CFU/ml). The G3 device can give (1) accurate bacterial counts, (2) serve as a growth chamber for bacteria, and (3) allow for live/dead bacterial cell estimates using staining kits or growth assay activities (live imaging, cell tracking, and counting). We made these devices out of necessity; we know no device on the market that encompasses all these features.

Radial compressed sensing imaging improves the velocity detection limit of single cell tracking time-lapse MRI.

PURPOSE: Time-lapse MRI enables tracking of single iron-labeled cells. Yet, due to temporal blurring, only slowly moving cells can be resolved. To study faster cells for example during inflammatory processes, accelerated acquisition is needed. METHODS: A rotating phantom system was developed to quantitatively measure the current maximum detectable speed of cells in time-lapse MRI. For accelerated cell tracking, an interleaved radial acquisition scheme was applied to phantom and murine brain in vivo time-lapse MRI experiments at 9.4 T. Detection of iron-labeled cells was evaluated in fully sampled and undersampled reconstructions with and without compressed sensing. RESULTS: The rotating phantom system enabled ultra-slow rotation of phantoms and a velocity detection limit of full-brain Cartesian time-lapse MRI of up to 172 µm/min was determined. Both phantom and in vivo measurements showed that single cells can be followed dynamically using radial time-lapse MRI. Higher temporal resolution of undersampled reconstructions reduced geometric distortion, the velocity detection limit was increased to 1.1 mm/min in vitro, and previously hidden fast-moving cells were recovered. In the mouse brain after in vivo labeling, a total of 42 ± 4 cells were counted in fully sampled, but only 7 ± 1 in undersampled images due to streaking artifacts. Using compressed sensing 33 ± 4 cells were detected. CONCLUSION: Interleaved radial time-lapse MRI permits retrospective reconstruction of both fully sampled and accelerated images, enables single cell tracking at higher temporal resolution and recovers cells hidden before due to blurring. The velocity detection limit as determined with the rotating phantom system increased two- to three-fold compared to previous results.

Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection.

3D cell tracking in a living organism has a crucial role in live cell image analysis. Cell tracking in C. elegans has two difficulties. First, cell migration in a consecutive frame is large since they move their head during scanning. Second, cell detection is often inconsistent in consecutive frames due to touching cells and low-contrast images, and these inconsistent detections affect the tracking performance worse. In this paper, we propose a cell tracking method to address these issues, which has two main contributions. First, we introduce cell position heatmap-based non-rigid alignment with test-time fine-tuning, which can warp the detected points to near the positions at the next frame. Second, we propose a pairwise detection method, which uses the information of detection results at the previous frame for detecting cells at the current frame. The experimental results demonstrate the effectiveness of each module, and the proposed method achieved the best performance in comparison.

Optical and MRI Multimodal Tracing of Stem Cells In Vivo.

Stem cell therapy has shown great clinical potential in oncology, injury, inflammation, and cardiovascular disease. However, due to the technical limitations of the in vivo visualization of transplanted stem cells, the therapeutic mechanisms and biosafety of stem cells in vivo are poorly defined, which limits the speed of clinical translation. The commonly used methods for the in vivo tracing of stem cells currently include optical imaging, magnetic resonance imaging (MRI), and nuclear medicine imaging. However, nuclear medicine imaging involves radioactive materials, MRI has low resolution at the cellular level, and optical imaging has poor tissue penetration in vivo. It is difficult for a single imaging method to simultaneously achieve the high penetration, high resolution, and noninvasiveness needed for in vivo imaging. However, multimodal imaging combines the advantages of different imaging modalities to determine the fate of stem cells in vivo in a multidimensional way. This review provides an overview of various multimodal imaging technologies and labeling methods commonly used for tracing stem cells, including optical imaging, MRI, and the combination of the two, while explaining the principles involved, comparing the advantages and disadvantages of different combination schemes, and discussing the challenges and prospects of human stem cell tracking techniques.

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.