Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Precise Serial Microregistration Enables Quantitative Microscopy Imaging Tracking of Human Skin Cells In Vivo.

We developed an automated microregistration method that enables repeated in vivo skin microscopy imaging of the same tissue microlocation and specific cells over a long period of days and weeks with unprecedented precision. Applying this method in conjunction with an in vivo multimodality multiphoton microscope, the behavior of human skin cells such as cell proliferation, melanin upward migration, blood flow dynamics, and epidermal thickness adaptation can be recorded over time, facilitating quantitative cellular dynamics analysis. We demonstrated the usefulness of this method in a skin biology study by successfully monitoring skin cellular responses for a period of two weeks following an acute exposure to ultraviolet light.

Digital in-line holographic microscopy for label-free identification and tracking of biological cells.

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.

Automatic classification of normal and abnormal cell division using deep learning.

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives like the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which cells are segmented and followed, and parental relationships annotated. However, DNA damage induced by genotoxic drugs or ionizing radiation produces additional abnormal events since it leads to behaviors like abnormal cell divisions (resulting in a number of daughters different from two) and cell death. With this in mind, we developed an automatic mitosis classifier to categorize small mitosis image sequences centered around one cell as "Normal" or "Abnormal." These mitosis sequences were extracted from videos of cell populations exposed to varying levels of radiation that affect the cell cycle's development. We explored several deep-learning architectures and found that a network with a ResNet50 backbone and including a Long Short-Term Memory (LSTM) layer produced the best results (mean F1-score: 0.93 ± 0.06). In the future, we plan to integrate this classifier with cell segmentation and tracking to build phylogenetic trees of the population after genomic stress.

Ultrasensitive and multiplexed tracking of single cells using whole-body PET/CT.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

B Cells of Early-life Origin Defined by RAG2-based Lymphoid Cell Tracking under Native Hematopoietic Conditions.

During the perinatal period, the immune system sets the threshold to select either response or tolerance to environmental Ags, which leads to the potential to provide a lifetime of protection and health. B-1a B cells have been demonstrated to develop during this perinatal time window, showing a unique and restricted BCR repertoire, and these cells play a major role in natural Ab secretion and immune regulation. In the current study, we developed a highly efficient temporally controllable RAG2-based lymphoid lineage cell labeling and tracking system and applied this system to understand the biological properties and contribution of B-1a cells generated at distinct developmental periods to the adult B-1a compartments. This approach revealed that B-1a cells with a history of RAG2 expression during the embryonic and neonatal periods dominate the adult B-1a compartment, including those in the bone marrow (BM), peritoneal cavity, and spleen. Moreover, the BCR repertoire of B-1a cells with a history of RAG2 expression during the embryonic period was restricted, becoming gradually more diverse during the neonatal period, and then heterogeneous at the adult stage. Furthermore, more than half of plasmablasts/plasma cells in the adult BM had embryonic and neonatal RAG2 expression histories. Moreover, BCR analysis revealed a high relatedness between BM plasmablasts/plasma cells and B-1a cells derived from embryonic and neonatal periods, suggesting that these cell types have a common origin. Taken together, these findings define, under native hematopoietic conditions, the importance in adulthood of B-1a cells generated during the perinatal period.

A semi-automated cell tracking protocol for quantitative analyses of neutrophil swarming to sterile and S. aureus contaminated bone implants in a mouse femur model.

Implant-associated osteomyelitis remains a major orthopaedic problem. As neutrophil swarming to the surgical site is a critical host response to prevent infection, visualization and quantification of this dynamic behavior at the native microenvironment of infection will elucidate previously unrecognized mechanisms central to understanding the host response. We recently developed longitudinal intravital imaging of the bone marrow (LIMB) to visualize host cells and fluorescent S. aureus on a contaminated transfemoral implant in live mice, which allows for direct visualization of bacteria colonization of the implant and host cellular responses using two-photon laser scanning microscopy. To the end of rigorous and reproducible quantitative outcomes of neutrophil swarming kinetics in this model, we developed a protocol for robust segmentation, tracking, and quantifications of neutrophil dynamics adapted from Trainable Weka Segmentation and TrackMate, two readily available Fiji/ImageJ plugins. In this work, Catchup mice with tdTomato expressing neutrophils received a transfemoral pin with or without ECFP/EGFP-expressing USA300 methicillin-resistant Staphylococcus aureus (MRSA) to obtain 30-minute LIMB videos at 2-, 4-, and 6-hours post-implantation. The developed semi-automated neutrophil tracking protocol was executed independently by two users to quantify the distance, displacement, speed, velocity, and directionality of the target cells. The results revealed high inter-user reliability for all outcomes (ICC > 0.96; p > 0.05). Consistent with the established paradigm on increased neutrophil swarming during active infection, the results also demonstrated increased neutrophil speed and velocity at all measured time points, and increased displacement at later time points (6 hours) in infected versus uninfected mice (p < 0.05). Neutrophils and bacteria also exhibit directionality during migration in the infected mice. The semi-automated cell tracking protocol provides a streamlined approach to robustly identify and track individual cells across diverse experimental settings and eliminates inter-observer variability.

Intravital Microscopy for Imaging and Live Cell Tracking of Alveolar Macrophages in Real Time.

Classic in vitro coculture assays of pathogens with host cells have contributed significantly to our understanding of the intracellular lifestyle of several pathogens. Coculture assays with pathogens and eukaryotic cells can be analyzed through various techniques including plating for colony-forming units (CFU), confocal microscopy, and flow cytometry. However, findings from in vitro assays require validation in an in vivo model. Several physiological conditions can influence host-pathogen interactions, which cannot easily be mimicked in vitro. Intravital microscopy (IVM) is emerging as a powerful tool for studying host-pathogen interactions by enabling in vivo imaging of living organisms. As a result, IVM has significantly enhanced the understanding of infection mediated by diverse pathogens. The versatility of IVM has also allowed for the imaging of various organs as sites of local infection. This chapter specifically focuses on IVM conducted on the lung for elucidating pulmonary immune response, primarily involving alveolar macrophages, to pathogens. Additionally, in this chapter we outline the protocol for lung IVM that utilizes a thoracic suction window to stabilize the lung for acquiring stable images.

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.

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.

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.

Fluorescence tracking demonstrates T cell recirculation is transiently impaired by radiation therapy to the tumor.

T cells recirculate through tissues and lymphatic organs to scan for their cognate antigen. Radiation therapy provides site-specific cytotoxicity to kill cancer cells but also has the potential to eliminate the tumor-specific T cells in field. To dynamically study the effect of radiation on CD8 T cell recirculation, we used the Kaede mouse model to photoconvert tumor-infiltrating cells and monitor their movement out of the field of radiation. We demonstrate that radiation results in loss of CD8 T cell recirculation from the tumor to the lymph node and to distant sites. Using scRNASeq, we see decreased proliferating CD8 T cells in the tumor following radiation therapy resulting in a proportional enrichment in exhausted phenotypes. By contrast, 5 days following radiation increased recirculation of T cells from the tumor to the tumor draining lymph node corresponds with increased immunosurveillance of the treated tumor. These data demonstrate that tumor radiation therapy transiently impairs systemic T cell recirculation from the treatment site to the draining lymph node and distant untreated tumors. This may inform timing therapies to improve systemic T cell-mediated tumor immunity.

Isolated Random Forest Assisted Spatio-Temporal Ant Colony Evolutionary Algorithm for Cell Tracking in Time-Lapse Sequences.

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

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.

Protocol for transcutaneous tumor photolabeling to track immune cells in vivo using Kaede mice.

Preclinical tumor models have advanced our understanding of the tumor microenvironment. However, the temporal dynamics of cellular recruitment and retention within these models is poorly understood. Here, we present a protocol using transcutaneous labeling of the tumor compartment using subcutaneous and orthotopic tumors. We describe the process of cell line implantation and photoconversion of tumors to differentiate newly recruited cells from those retained within tumors. Photoconversion enables tracking of both immune cell recruitment to tumors and egress to the lymphatics. For complete details on the use and execution of this protocol, please refer to Li et al.1 and Molostvov et al.2.

Longitudinal Magnetic Resonance Imaging Tracking of Transplanted Neural Progenitor Cells in the Spinal Cord Utilizing the Bright-Ferritin Mechanism.

Human neural progenitor cells (hNPCs) hold promise for treating spinal cord injury. Studies to date have focused on improving their regenerative potential and therapeutic effect. Equally important is ensuring successful delivery and engraftment of hNPCs at the injury site. Unfortunately, no current imaging solution for cell tracking is compatible with long-term monitoring in vivo. The objective of this study was to apply a novel bright-ferritin magnetic resonance imaging (MRI) mechanism to track hNPC transplants longitudinally and on demand in the rat spinal cord. We genetically modified hNPCs to stably overexpress human ferritin. Ferritin-overexpressing (FT) hNPCs labeled with 0.2 mM manganese provided significant T1-induced bright contrast on in vitro MRI, with no adverse effect on cell viability, morphology, proliferation, and differentiation. In vivo, 2 M cells were injected into the cervical spinal cord of Rowett nude rats. MRI employed T1-weighted acquisitions and T1 mapping on a 3 T scanner. Conventional short-term cell tracking was performed using exogenous Mn labeling prior to cell transplantation, which displayed transient bright contrast on MRI 1 day after cell transplantation and disappeared after 1 week. In contrast, long-term cell tracking using bright-ferritin allowed on-demand signal recall upon Mn supplementation and precise visualization of the surviving hNPC graft. In fact, this new cell tracking technology identified 7 weeks post-transplantation as the timepoint by which substantial hNPC integration occurred. Spatial distribution of hNPCs on MRI matched that on histology. In summary, bright-ferritin provides the first demonstration of long-term, on-demand, high-resolution, and specific tracking of hNPCs in the rat spinal cord.

Single-cell lineage tracing approaches to track kidney cell development and maintenance.

The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.

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.