Development of quantitative, safe and rapid techniques for assessing embryo quality provides significant advances in Assisted Reproductive Technologies (ART). Instead of assessing the embryo quality by the standard morphologic evaluation, we apply the phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) method to capture endogenous fluorescent biomarkers of pre-implantation embryos as a non-morphological caliber for embryo quality. Here, we identify, under hypoxic and non-hypoxic conditions, the unique spectroscopic trajectories at different stages of mouse pre-implantation development, which is referred to as the developmental, or “D-trajectory”, that consists of fluorescence lifetime from different stages of mouse pre-implantation embryos. The D-trajectory correlates with intrinsic fluorescent species from a distinctive energy metabolism and oxidized lipids, as seen with Third Harmonic Generation (THG) that changes over time. In addition, we have defined a non-morphological Embryo Viability Index (EVI) to distinguish pre-implantation embryo quality using the Distance Analysis (DA), a machine learning algorithm to process the fluorescence lifetime distribution patterns. We show, under our experimental conditions, that the phasor-FLIM approach provides a much-needed non-invasive quantitative technology for identifying healthy embryos at the early compaction stage with 86% accuracy. The DA and phasor-FLIM method may provide the opportunity to improve implantation success rates for in vitro fertilization clinics.

Senescent cells are recognized drivers of aging-related decline in organ function, but deciphering the biology of senescence in vivo has been hindered by the paucity of tools to track and isolate senescent cells in tissues1–4. Deleting senescent cells from transgenic murine models have demonstrated therapeutic benefits in numerous age-related diseases5–11, but the identity, behavior, and function of the senescent cells deleted in vivo remain elusive. We engineered an ultra-sensitive reporter of p16INK4a, a biomarker of senescence12, to isolate and track p16INK4a+ cells in vivo. Surprisingly, p16INK4a+ mesenchymal cells appear in the basement membrane adjacent to epithelial progenitors in the lung shortly after birth, and these cells demonstrate senescent characteristics in vivo and ex vivo. Transcriptomic analysis of p16INK4a+ mesenchymal cells from non-aged lungs demonstrates a transition to a secretory phenotype upon airway epithelial injury. Heterotypic 3D organoid assays show that injured p16INK4a+ mesenchymal cells enhance epithelial progenitor proliferation, and we identified EREG as a novel airway progenitor mitogen produced by the secretory p16INK4a+ mesenchymal cells. Mesenchymal-specific deletion of the p16INK4a gene abrogates features of senescence in vivo, but also attenuates normal epithelial repair. Thus, p16INK4a+ mesenchymal cells can act as sentinels for the airway epithelial stem cell niche, poised to transition to a senescence-associated secretory phenotype to support barrier repair. Our data identify possible cellular targets in vivo for a rapidly growing list of senolytic therapies, but also raises important questions about the hidden cost of targeting senescent cells present in normal organs.

BackgroundAnalysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge.ResultsHere we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels.ConclusionsHigh-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights — in particular with respect to the cell cycle — that would be difficult to derive otherwise.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0814-7) contains supplementary material, which is available to authorized users.

Highlights:• A label-free method of tracking metabolic trajectories during pre-implantation mouse embryo development.• A non-invasive approach for assessing embryo quality and viability by a phasor-FLIM analysis. AbstractDevelopment of quantitative, safe and rapid techniques for assessing embryo quality provides significant advances in Assisted Reproductive Technologies (ART). We apply the phasor-FLIM method to capture endogenous fluorescent biomarkers of pre-implantation embryos as a non-morphological caliber for embryo quality. Here, we identify the developmental, or "D-trajectory", that consists of fluorescence lifetime from different stages of mouse pre-implantation embryos. The D-trajectory correlates with intrinsic fluorescent species from a distinctive energy metabolism and oxidized lipids, as seen with Third Harmonic Generation (THG) that changes over time. In addition, we have defined an Embryo Viability Index (EVI) to distinguish pre-implantation embryo quality using the Distance Analysis, a machine learning algorithm to process the fluorescence lifetime distribution patterns. We show that the phasor-FLIM approach provides a much-needed non-invasive quantitative technology for identifying healthy embryos at the early compaction stage with 86% accuracy. This may increase embryo implantation success for in vitro fertilization clinics.