Ultralow-dose irradiation enables engraftment and intravital tracking of disease initiating niches in clonal hematopoiesis.

Recent advances in imaging suggested that spatial organization of hematopoietic cells in their bone marrow microenvironment (niche) regulates cell expansion, governing progression, and leukemic transformation of hematological clonal disorders. However, our ability to interrogate the niche in pre-malignant conditions has been limited, as standard murine models of these diseases rely largely on transplantation of the mutant clones into conditioned mice where the marrow microenvironment is compromised. Here, we leveraged live-animal microscopy and ultralow dose whole body or focal irradiation to capture single cells and early expansion of benign/pre-malignant clones in the functionally preserved microenvironment. 0.5 Gy whole body irradiation (WBI) allowed steady engraftment of cells beyond 30 weeks compared to non-conditioned controls. In-vivo tracking and functional analyses of the microenvironment showed no change in vessel integrity, cell viability, and HSC-supportive functions of the stromal cells, suggesting minimal inflammation after the radiation insult. The approach enabled in vivo imaging of Tet2+/- and its healthy counterpart, showing preferential localization within a shared microenvironment while forming discrete micro-niches. Notably, stationary association with the niche only occurred in a subset of cells and would not be identified without live imaging. This strategy may be broadly applied to study clonal disorders in a spatial context.

Identification of an embryonic differentiation stage marked by Sox1 and FoxA2 co-expression using combined cell tracking and high dimensional protein imaging.

Pluripotent mouse embryonic stem cells (ESCs) can differentiate to all germ layers and serve as an in vitro model of embryonic development. To better understand the differentiation paths traversed by ESCs committing to different lineages, we track individual differentiating ESCs by timelapse imaging followed by multiplexed high-dimensional Imaging Mass Cytometry (IMC) protein quantification. This links continuous live single-cell molecular NANOG and cellular dynamics quantification over 5-6 generations to protein expression of 37 different molecular regulators in the same single cells at the observation endpoints. Using this unique data set including kinship history and live lineage marker detection, we show that NANOG downregulation occurs generations prior to, but is not sufficient for neuroectoderm marker Sox1 upregulation. We identify a developmental cell type co-expressing both the canonical Sox1 neuroectoderm and FoxA2 endoderm markers in vitro and confirm the presence of such a population in the post-implantation embryo. RNASeq reveals cells co-expressing SOX1 and FOXA2 to have a unique cell state characterized by expression of both endoderm as well as neuroectoderm genes suggesting lineage potential towards both germ layers.

Tracking of lineage mass via quantitative phase imaging and confinement in low refractive index microwells.

Measurements of cell lineages are central to a variety of fundamental biological questions, ranging from developmental to cancer biology. However, accurate lineage tracing requires nearly perfect cell tracking, which can be challenging due to cell motion during imaging. Here we demonstrate the integration of microfabrication, imaging, and image processing approaches to demonstrate a platform for cell lineage tracing. We use quantitative phase imaging (QPI), a label-free imaging approach that quantifies cell mass. This gives an additional parameter, cell mass, that can be used to improve tracking accuracy. We confine lineages within microwells fabricated to reduce cell adhesion to sidewalls made of a low refractive index polymer. This also allows the microwells themselves to serve as references for QPI, enabling measurement of cell mass even in confluent microwells. We demonstrate application of this approach to immortalized adherent and nonadherent cell lines as well as stimulated primary B cells cultured ex vivo. Overall, our approach enables lineage tracking, or measurement of lineage mass, in a platform that can be customized to varied cell types.

CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data.

In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.

In Vivo Tracking and 3D Mapping of Cell Death in Regeneration and Cancer Using Trypan Blue.

Tracking cell death in vivo can enable a better understanding of the biological mechanisms underlying tissue homeostasis and disease. Unfortunately, existing cell death labeling methods lack compatibility with in vivo applications or suffer from low sensitivity, poor tissue penetration, and limited temporal resolution. Here, we fluorescently labeled dead cells in vivo with Trypan Blue (TBlue) to detect single scattered dead cells or to generate whole-mount three-dimensional maps of large areas of necrotic tissue during organ regeneration. TBlue effectively marked different types of cell death, including necrosis induced by CCl4 intoxication in the liver, necrosis caused by ischemia-reperfusion in the skin, and apoptosis triggered by BAX overexpression in hepatocytes. Moreover, due to its short circulating lifespan in blood, TBlue labeling allowed in vivo "pulse and chase" tracking of two temporally spaced populations of dying hepatocytes in regenerating mouse livers. Additionally, upon treatment with cisplatin, TBlue labeled dead cancer cells in livers with cholangiocarcinoma and dead thymocytes due to chemotherapy-induced toxicity, showcasing its utility in assessing anticancer therapies in preclinical models. Thus, TBlue is a sensitive and selective cell death marker for in vivo applications, facilitating the understanding of the fundamental role of cell death in normal biological processes and its implications in disease.

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.

A Live Tracker of Dormant Disseminated Tumor Cells.

The high prevalence of dormant disseminated tumor cells (DTCs) persisting systemically in patients with metastatic cancer is a major threat to long-lasting cure (Aguirre-Ghiso, Nat Rev Cancer 7:834-846, 2007; Klein, Nat Rev Cancer 20(11):681-694, 2020; Lyden et al. Cancer Cell 40:787-791, 2022). Despite its clinical significance, the study of what drives DTCs in and out of dormancy while they linger in distant sites has been challenged by the lack of tools to find and follow dormant DTCs inside a living organism. Here, leveraging the fact that dormant DTCs are mostly quiescent, we describe a live cell reporter to distinguish dormant from cycling DTCs (Correia, Nat Rev Cancer 22(7):379, 2022; Correia et al. Nature 594(7864):566-571, 2021). Cancer cell lines are engineered to coexpress a luciferase-tdTomato reporter and a fluorescent fusion protein of mVenus with a mutant form of the cell cycle inhibitor p27 (mVenus-p27K-) that identifies quiescent cells. When implanted in animal models or assembled in cocultures in vitro, labeled cells can be imaged longitudinally over time or retrieved alive alongside their surrounding microenvironment for downstream gene, protein, and metabolite profiling, allowing the mapping of tissue-specific determinants of cancer dormancy and metastasis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.