Intravital three-photon microscopy allows visualization over the entire depth of mouse lymph nodes.

Intravital confocal microscopy and two-photon microscopy are powerful tools to explore the dynamic behavior of immune cells in mouse lymph nodes (LNs), with penetration depth of ~100 and ~300 µm, respectively. Here, we used intravital three-photon microscopy to visualize the popliteal LN through its entire depth (600-900 µm). We determined the laser average power and pulse energy that caused measurable perturbation in lymphocyte migration. Long-wavelength three-photon imaging within permissible parameters was able to image the entire LN vasculature in vivo and measure CD8+ T cells and CD4+ T cell motility in the T cell zone over the entire depth of the LN. We observed that the motility of naive CD4+ T cells in the T cell zone during lipopolysaccharide-induced inflammation was dependent on depth. As such, intravital three-photon microscopy had the potential to examine immune cell behavior in the deeper regions of the LN in vivo.

In situ mapping identifies distinct vascular niches for myelopoiesis.

In contrast to nearly all other tissues, the anatomy of cell differentiation in the bone marrow remains unknown. This is owing to a lack of strategies for examining myelopoiesis-the differentiation of myeloid progenitors into a large variety of innate immune cells-in situ in the bone marrow. Such strategies are required to understand differentiation and lineage-commitment decisions, and to define how spatial organizing cues inform tissue function. Here we develop approaches for imaging myelopoiesis in mice, and generate atlases showing the differentiation of granulocytes, monocytes and dendritic cells. The generation of granulocytes and dendritic cells-monocytes localizes to different blood-vessel structures known as sinusoids, and displays lineage-specific spatial and clonal architectures. Acute systemic infection with Listeria monocytogenes induces lineage-specific progenitor clusters to undergo increased self-renewal of progenitors, but the different lineages remain spatially separated. Monocyte-dendritic cell progenitors (MDPs) map with nonclassical monocytes and conventional dendritic cells; these localize to a subset of blood vessels expressing a major regulator of myelopoiesis, colony-stimulating factor 1 (CSF1, also known as M-CSF)1. Specific deletion of Csf1 in endothelium disrupts the architecture around MDPs and their localization to sinusoids. Subsequently, there are fewer MDPs and their ability to differentiate is reduced, leading to a loss of nonclassical monocytes and dendritic cells during both homeostasis and infection. These data indicate that local cues produced by distinct blood vessels are responsible for the spatial organization of definitive blood cell differentiation.

The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

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.

Lineage tracing meets single-cell omics: opportunities and challenges.

A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.

Unravelling stem cell dynamics by lineage tracing.

During embryonic and postnatal development, the different cells types that form adult tissues must be generated and specified in a precise temporal manner. During adult life, most tissues undergo constant renewal to maintain homeostasis. Lineage-tracing and genetic labelling technologies are beginning to shed light on the mechanisms and dynamics of stem and progenitor cell fate determination during development, tissue maintenance and repair, as well as their dysregulation in tumour formation. Statistical approaches, based on proliferation assays and clonal fate analyses, provide quantitative insights into cell kinetics and fate behaviour. These are powerful techniques to address new questions and paradigms in transgenic mouse models and other model systems.

Metastatic-niche labelling reveals parenchymal cells with stem features.

Direct investigation of the early cellular changes induced by metastatic cells within the surrounding tissue remains a challenge. Here we present a system in which metastatic cancer cells release a cell-penetrating fluorescent protein, which is taken up by neighbouring cells and enables spatial identification of the local metastatic cellular environment. Using this system, tissue cells with low representation in the metastatic niche can be identified and characterized within the bulk tissue. To highlight its potential, we applied this strategy to study the cellular environment of metastatic breast cancer cells in the lung. We report the presence of cancer-associated parenchymal cells, which exhibit stem-cell-like features, expression of lung progenitor markers, multi-lineage differentiation potential and self-renewal activity. In ex vivo assays, lung epithelial cells acquire a cancer-associated parenchymal-cell-like phenotype when co-cultured with cancer cells and support their growth. These results highlight the potential of this method as a platform for new discoveries.

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.

Current approaches to fate mapping and lineage tracing using image data.

Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.

Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker.

Most bacteria live attached to surfaces in densely-packed communities. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms, these typically require users either to tune a large number of parameters or to groundtruth a large volume of imaging data to train a deep learning model-both manual processes which can be very time consuming for novel experiments. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing 'features' for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naïve approach that uses cell position alone revealed that FAST produced 4 to 10 fold fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.

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.

Whole-organism clone tracing using single-cell sequencing.

Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development, monitoring the heritable expression of genetically encoded fluorescent proteins and, more recently, using next-generation sequencing technologies that exploit somatic mutations, microsatellite instability, transposon tagging, viral barcoding, CRISPR-Cas9 genome editing and Cre-loxP recombination. Single-cell transcriptomics provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.

Single-cell mapping of lineage and identity in direct reprogramming.

Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present 'CellTagging', a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a 'dead-end' state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.

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.

Understanding generation and regeneration of pancreatic ß cells from a single-cell perspective.

Understanding the mechanisms that underlie the generation and regeneration of ß cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of ß-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding ß-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Isolating live cells after high-throughput, long-term, time-lapse microscopy.

Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.

pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

SUMMARY: Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single cells over cell division events. Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning-based fluorescent proliferative cell nuclear antigen signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. AVAILABILITY AND IMPLEMENTATION: pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of chimeric antigen receptor modified T cells expressing scFv-IL-13Rα2 after radiolabeling with 89Zirconium oxine for PET imaging.

BACKGROUND: Chimeric antigen receptor (CAR) T cell therapy is an exciting cell-based cancer immunotherapy. Unfortunately, CAR-T cell therapy is associated with serious toxicities such as cytokine release syndrome (CRS) and neurotoxicity. The mechanism of these serious adverse events (SAEs) and how homing, distribution and retention of CAR-T cells contribute to toxicities is not fully understood. Enabling in vitro methods to allow meaningful, sensitive in vivo biodistribution studies is needed to better understand CAR-T cell disposition and its relationship to both effectiveness and safety of these products. METHODS: To determine if radiolabelling of CAR-T cells could support positron emission tomography (PET)-based biodistribution studies, we labeled IL-13Rα2 targeting scFv-IL-13Rα2-CAR-T cells (CAR-T cells) with 89Zirconium-oxine (89Zr-oxine) and characterized and compared their product attributes with non-labeled CAR-T cells. The 89Zr-oxine labeling conditions were optimized for incubation time, temperature, and use of serum for labeling. In addition, T cell subtype characterization and product attributes of radiolabeled CAR-T cells were studied to assess their overall quality including cell viability, proliferation, phenotype markers of T-cell activation and exhaustion, cytolytic activity and release of interferon-γ upon co-culture with IL-13Rα2 expressing glioma cells. RESULTS: We observed that radiolabeling of CAR-T cells with 89Zr-oxine is quick, efficient, and radioactivity is retained in the cells for at least 8 days with minimal loss. Also, viability of radiolabeled CAR-T cells and subtypes such as CD4 + , CD8 + and scFV-IL-13Rα2 transgene positive T cell population were characterized and found similar to that of unlabeled cells as determined by TUNEL assay, caspase 3/7 enzyme and granzyme B activity assay. Moreover, there were no significant changes in T cell activation (CD24, CD44, CD69 and IFN-γ) or T cell exhaustion (PD-1, LAG-3 and TIM3) markers expression between radiolabeled and unlabeled CAR-T cells. In chemotaxis assays, migratory capability of radiolabeled CAR-T cells to IL-13Rα2Fc was similar to that of non-labeled cells. CONCLUSIONS: Importantly, radiolabeling has minimal impact on biological product attributes including potency of CAR-T cells towards IL-13Rα2 positive tumor cells but not IL-13Rα2 negative cells as measured by cytolytic activity and release of IFN-γ. Thus, IL-13Rα2 targeting CAR-T cells radiolabeled with 89Zr-oxine retain critical product attributes and suggest 89Zr-oxine radiolabeling of CAR-T cells may facilitate biodistribution and tissue trafficking studies in vivo using PET.

Polylox barcoding reveals haematopoietic stem cell fates realized in vivo.

Developmental deconvolution of complex organs and tissues at the level of individual cells remains challenging. Non-invasive genetic fate mapping has been widely used, but the low number of distinct fluorescent marker proteins limits its resolution. Much higher numbers of cell markers have been generated using viral integration sites, viral barcodes, and strategies based on transposons and CRISPR-Cas9 genome editing; however, temporal and tissue-specific induction of barcodes in situ has not been achieved. Here we report the development of an artificial DNA recombination locus (termed Polylox) that enables broadly applicable endogenous barcoding based on the Cre-loxP recombination system. Polylox recombination in situ reaches a practical diversity of several hundred thousand barcodes, allowing tagging of single cells. We have used this experimental system, combined with fate mapping, to assess haematopoietic stem cell (HSC) fates in vivo. Classical models of haematopoietic lineage specification assume a tree with few major branches. More recently, driven in part by the development of more efficient single-cell assays and improved transplantation efficiencies, different models have been proposed, in which unilineage priming may occur in mice and humans at the level of HSCs. We have introduced barcodes into HSC progenitors in embryonic mice, and found that the adult HSC compartment is a mosaic of embryo-derived HSC clones, some of which are unexpectedly large. Most HSC clones gave rise to multilineage or oligolineage fates, arguing against unilineage priming, and suggesting coherent usage of the potential of cells in a clone. The spreading of barcodes, both after induction in embryos and in adult mice, revealed a basic split between common myeloid-erythroid development and common lymphocyte development, supporting the long-held but contested view of a tree-like haematopoietic structure.

Multicolor strategies for investigating clonal expansion and tissue plasticity.

Understanding the generation of complexity in living organisms requires the use of lineage tracing tools at a multicellular scale. In this review, we describe the different multicolor strategies focusing on mouse models expressing several fluorescent reporter proteins, generated by classical (MADM, Brainbow and its multiple derivatives) or acute (StarTrack, CLoNe, MAGIC Markers, iOn, viral vectors) transgenesis. After detailing the multi-reporter genetic strategies that serve as a basis for the establishment of these multicolor mouse models, we briefly mention other animal and cellular models (zebrafish, chicken, drosophila, iPSC) that also rely on these constructs. Then, we highlight practical applications of multicolor mouse models to better understand organogenesis at single progenitor scale (clonal analyses) in the brain and briefly in several other tissues (intestine, skin, vascular, hematopoietic and immune systems). In addition, we detail the critical contribution of multicolor fate mapping strategies in apprehending the fine cellular choreography underlying tissue morphogenesis in several models with a particular focus on brain cytoarchitecture in health and diseases. Finally, we present the latest technological advances in multichannel and in-depth imaging, and automated analyses that enable to better exploit the large amount of data generated from multicolored tissues.