More Like this

1. Enhanced detection of oral dysplasia by structured illumination fluorescence lifetime imaging microscopy

   https://doi.org/10.1038/s41598-021-84552-8  

   Hinsdale, Taylor A. ; Malik, Bilal H. ; Cheng, Shuna ; Benavides, Oscar R. ; Giger, Maryellen L. ; Wright, John M. ; Patel, Paras B. ; Jo, Javier A. ; Maitland, Kristen C. ( December 2021 , Scientific Reports)

   Abstract We demonstrate that structured illumination microscopy has the potential to enhance fluorescence lifetime imaging microscopy (FLIM) as an early detection method for oral squamous cell carcinoma. FLIM can be used to monitor or detect changes in the fluorescence lifetime of metabolic cofactors (e.g. NADH and FAD) associated with the onset of carcinogenesis. However, out of focus fluorescence often interferes with this lifetime measurement. Structured illumination fluorescence lifetime imaging (SI-FLIM) addresses this by providing depth-resolved lifetime measurements, and applied to oral mucosa, can localize the collected signal to the epithelium. In this study, the hamster model of oral carcinogenesis wasmore »used to evaluate SI-FLIM in premalignant and malignant oral mucosa. Cheek pouches were imaged in vivo and correlated to histopathological diagnoses. The potential of NADH fluorescence signal and lifetime, as measured by widefield FLIM and SI-FLIM, to differentiate dysplasia (pre-malignancy) from normal tissue was evaluated. ROC analysis was carried out with the task of discriminating between normal tissue and mild dysplasia, when changes in fluorescence characteristics are localized to the epithelium only. The results demonstrate that SI-FLIM (AUC = 0.83) is a significantly better (p-value = 0.031) marker for mild dysplasia when compared to widefield FLIM (AUC = 0.63).« less
2. A coarse-grained NADH redox model enables inference of subcellular metabolic fluxes from fluorescence lifetime imaging

   https://doi.org/10.7554/eLife.73808  

   Yang, Xingbo ; Ha, Gloria ; Needleman, Daniel J ( November 2021 , eLife)

   Mitochondrial metabolism is of central importance to diverse aspects of cell and developmental biology. Defects in mitochondria are associated with many diseases, including cancer, neuropathology, and infertility. Our understanding of mitochondrial metabolism in situ and dysfunction in diseases are limited by the lack of techniques to measure mitochondrial metabolic fluxes with sufficient spatiotemporal resolution. Herein, we developed a new method to infer mitochondrial metabolic fluxes in living cells with subcellular resolution from fluorescence lifetime imaging of NADH. This result is based on the use of a generic coarse-grained NADH redox model. We tested the model in mouse oocytes and humanmore »tissue culture cells subject to a wide variety of perturbations by comparing predicted fluxes through the electron transport chain (ETC) to direct measurements of oxygen consumption rate. Interpreting the fluorescence lifetime imaging microscopy measurements of NADH using this model, we discovered a homeostasis of ETC flux in mouse oocytes: perturbations of nutrient supply and energy demand of the cell do not change ETC flux despite significantly impacting NADH metabolic state. Furthermore, we observed a subcellular spatial gradient of ETC flux in mouse oocytes and found that this gradient is primarily a result of a spatially heterogeneous mitochondrial proton leak. We concluded from these observations that ETC flux in mouse oocytes is not controlled by energy demand or supply, but by the intrinsic rates of mitochondrial respiration.« less
3. Enhanced drug delivery to the reproductive tract using nanomedicine reveals therapeutic options for prevention of preterm birth

   https://doi.org/10.1126/scitranslmed.abc6245  

   Zierden, Hannah C. ; Ortiz, Jairo I. ; DeLong, Kevin ; Yu, Jingqi ; Li, Gaoshan ; Dimitrion, Peter ; Bensouda, Sabrine ; Laney, Victoria ; Bailey, Anna ; Anders, Nicole M. ; et al ( January 2021 , Science Translational Medicine)

   Inflammation contributes to nearly 4 million global premature births annually. Here, we used a mouse model of intrauterine inflammation to test clinically used formulations, as well as engineered nanoformulations, for the prevention of preterm birth (PTB). We observed that neither systemic 17a-hydroxyprogesterone caproate (Makena) nor vaginal progesterone gel (Crinone) was sufficient to prevent inflammation-induced PTB, consistent with recent clinical trial failures. However, we found that vaginal delivery of mucoinert nanosuspensions of histone deacetylase (HDAC) inhibitors, in some cases with the addition of progesterone, prevented PTB and resulted in delivery of live pups exhibiting neurotypical development. In human myometrial cells inmore »vitro, the P4/HDAC inhibitor combination both inhibited cell contractility and promoted the anti-inflammatory action of P4 by increasing progesterone receptor B stability. Here, we demonstrate the use of vaginally delivered drugs to prevent intrauterine inflammation–induced PTB resulting in the birth of live offspring in a preclinical animal model.

   « less
4. Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records

   https://doi.org/10.1038/s41598-022-04814-x  

   Xu, Tingting ; de Figueiredo Veiga, Alexis ; Hammer, Karissa C. ; Paschalidis, Ioannis Ch. ; Mahalingaiah, Shruthi ( December 2022 , Scientific Reports)

   Abstract The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort wasmore »the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /− 0.01%. A parsimonious predictive model utilizing age (38–40, 41–42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /− 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.« less
5. Machine learning-assisted imaging analysis of a human epiblast model

   https://doi.org/10.1093/intbio/zyab014  

   Resto Irizarry, Agnes M ; Esfahani, Sajedeh Nasr ; Zheng, Yi ; Yan, Robin Zhexuan ; Kinnunen, Patrick ; Fu, Jianping ( July 2021 , Integrative Biology)

   Abstract The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell–cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from amore »human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell–cell and cell–environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.« less