During in vitro fertilization (IVF) cycles, multiple mature oocytes are retrieved from the ovary and are fertilized in the lab. The newly generated embryos can be transferred into the uterus on day-3,-4, or-5 of incubation, cryopreserved for subsequent transfers or discarded. Lacking a reliable noninvasive evaluation method of the potential to implant, pregnancy rates can be improved by cotransferring multiple embryos thus introducing health risks that are associated with multiple pregnancies. [1] Hence, the evaluation of embryo quality is required for improving live birth rates while minimizing medical complications and shortening time to pregnancy. [2-6] Machine learning was used for assessing the potential of embryos to blastulate [7,8] and to implant [9-11] based on manually annotated morphological and/or morphokientic features. Deep learning, which offers a powerful toolbox for carrying out automated and standardized classification tasks

Mesenchymal stromal/stem cells (MSCs) form a heterogeneous population of multipotent progenitors that contribute to tissue regeneration and homeostasis. MSCs assess extracellular elasticity by probing resistance to applied forces via adhesion, cytoskeletal, and nuclear mechanotransducers that direct differentiation toward soft or stiff tissue lineages. Even under controlled culture conditions, MSC differentiation exhibits substantial cell-to-cell variation that remains poorly characterized. By single-cell transcriptional profiling of nonconditioned, matrix-conditioned, and early differentiating cells, we identified distinct MSC subpopulations with distinct mechanosensitivities, differentiation capacities, and cell cycling. We show that soft matrices support adipogenesis of multipotent cells and early endochondral ossification of nonadipogenic cells, whereas intramembranous ossification and preosteoblast proliferation are directed by stiff matrices. Using diffusion pseudotime mapping, we outline hierarchical matrix-directed differentiation and perform whole-genome screening of mechanoresponsive genes. Specifically, top-ranked tropomyosin-1 is highly sensitive to stiffness cues both at RNA and protein levels, and changes in TPM1 expression determine the differentiation toward soft versus stiff tissue lineage. Consistent with actin stress fiber stabilization, tropomyosin-1 overexpression maintains YAP1 nuclear localization, activates YAP1 target genes, and directs osteogenic differentiation. Knockdown of tropomyosin-1 reversed YAP1 nuclear localization consistent with relaxation of cellular contractility, suppressed osteogenesis, activated early endochondral ossification genes after 3 d of culture in induction medium, and facilitated adipogenic differentiation after 1 wk. Our results delineate cell-to-cell variation of matrix-directed MSC differentiation and highlight tropomyosin-mediated matrix sensing.

Background It is unclear whether sperm origin, either ejaculated or testicular, in couples diagnosed with male factor infertility, affects the timing of the embryo's developmental events evaluated by time‐lapse monitoring and implantation rates. Objective To examine the effect of sperm origin on embryo morphokinetics in couples diagnosed with male factor infertility. Materials and Methods This study included a retrospective analysis of morphokinetic parameters performed by time‐lapse monitoring between 2013 and 2017. The developmental processes and morphokinetic parameters of 419 embryos obtained from couples with male factor infertility attributed to oligo‐astheno‐teratozoospermia, 158 embryos derived from surgically extracted testicular spermatozoa from couples diagnosed with non‐obstructive azoospermia, and 190 embryos from couples with normal ejaculated spermatozoa and female mechanical factor‐related infertility, were evaluated. A comparison of morphokinetic parameters, implantation, and clinical pregnancy rates was performed between the groups with additional analysis in accordance with implantation status. Results Embryos from the normal ejaculated spermatozoa and oligo‐astheno‐teratozoospermia patients reached the later morphokinetic milestones—synchronous division (S3) and time to morula (tM)—faster than embryos obtained from testicular spermatozoa. Implantation rate was similar in the normal ejaculated spermatozoa and oligo‐astheno‐teratozoospermia groups (41.9% vs. 45.8%, NS), with higher implantation rate in the oligo‐astheno‐teratozoospermia group compared to the testicular spermatozoa group (45.8% vs. 33.6%, p = 0.02). Comparison of Known Implantation Data (KID) positive (KIDp) and KID negative (KIDn) embryos in each group revealed more rapid development in KIDp embryos in the normal ejaculated spermatozoa and the oligo‐astheno‐teratozoospermia groups, while in the testicular spermatozoa group implanted embryos reached the late morphokinetic milestones (time to 8 cell stage—t8, ECC3, S3, and tM) significantly faster than embryos that failed to implant. In a multivariate logistic regression analysis of the male factor infertility population, (oligo‐astheno‐teratospermia) (OR = 2.54, p = 0.003) and t8 (OR = 0.95, p = 0.027) were predictive of successful implantation. Male factor infertility embryos that reached the t8 milestone within 48–56 h had favorable implantation rates (p < 0.001). Discussion The study results may highlight another pathophysiology by means of which sperm origin affects embryo developmental kinetics. Selecting embryos demonstrating a faster developmental rate at t8 and specifically the 48‐ to 56 h interval following time of pronuclei fading (tPNf) may improve implantation rates in cases of male factor infertility. Conclusion This study showed that ejaculated spermatozoa is associated with faster late cell divisions, more rapid compaction, and higher implantation rates compared to testicular spermatozoa. Additionally, t8 is an important predictor for implantation in the m...

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The majority of human embryos, whether naturally or in vitro fertilized (IVF), do not poses the capacity to implant within the uterus and reach live birth. Hence, selecting the embryos with the highest developmental potential to implant is imperative for improving pregnancy rates without prolonging time to pregnancy. The developmental potential of embryos can be assessed based on temporal profiling of the discrete morphokinetic events of preimplantation development. However, manual morphokinetic annotation introduces intra- and inter-observer variation and is time-consuming. Using a large clinically-labeled multicenter dataset of video recordings of preimplantation embryo development by time-lapse incubators, we trained a convolutional neural network and developed a classifier that performs fully automated, robust, and standardized annotation of the morphokinetic events with R-square 0.994 accuracy. To delineate the morphokinetic heterogeneity of preimplantation development, we performed unsupervised clustering of high-quality embryo candidates for transfer, which was independent of maternal age and blastulation rate. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters that are distinctively marked by poor synchronization of the third meiotic cell-cleavage cycle. We expect this work to advance the integration of morphokinetic-based decision support tools in IVF treatments and deepen our understanding of preimplantation heterogeneity.