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.

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.

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.

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.

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.

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.

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.

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.

暂无摘要。

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.