Single-cell characterization of self-renewing primary trophoblast organoids as modeling of EVT differentiation and interactions with decidual natural killer cells.

BACKGROUND: Extravillous trophoblast cell (EVT) differentiation and its communication with maternal decidua especially the leading immune cell type natural killer (NK) cell are critical events for placentation. However, appropriate in vitro modelling system and regulatory programs of these two events are still lacking. Recent trophoblast organoid (TO) has advanced the molecular and mechanistic research in placentation. Here, we firstly generated the self-renewing TO from human placental villous and differentiated it into EVTs (EVT-TO) for investigating the differentiation events. We then co-cultured EVT-TO with freshly isolated decidual NKs for further study of cell communication. TO modelling of EVT differentiation as well as EVT interaction with dNK might cast new aspect for placentation research. RESULTS: Single-cell RNA sequencing (scRNA-seq) was applied for comprehensive characterization and molecular exploration of TOs modelling of EVT differentiation and interaction with dNKs. Multiple distinct trophoblast states and dNK subpopulations were identified, representing CTB, STB, EVT, dNK1/2/3 and dNKp. Lineage trajectory and Seurat mapping analysis identified the close resemblance of TO and EVT-TO with the human placenta characteristic. Transcription factors regulatory network analysis revealed the cell-type specific essential TFs for controlling EVT differentiation. CellphoneDB analysis predicted the ligand-receptor complexes in dNK-EVT-TO co-cultures, which relate to cytokines, immunomodulation and angiogenesis. EVT was known to affect the immune properties of dNK. Our study found out that on the other way around, dNKs could exert effects on EVT causing expression changes which are functionally important. CONCLUSION: Our study documented a single-cell atlas for TO and its applications on EVT differentiation and communications with dNKs, and thus provide methodology and novel research cues for future study of human placentation.

Derivation of trophoblast stem cells from human expanded potential stem cells.

Human trophoblast development study has long been limited by the lack of suitable materials. Here we present a detailed protocol for the differentiation of human expanded potential stem cells (hEPSCs) into human trophoblast stem cells (TSCs) and for the subsequent establishment of TSC lines. The hEPSC-derived TSC lines can be continuously passaged and are functional in further differentiation into syncytiotrophoblasts and extravillous trophoblasts. The hEPSC-TSC system offers a valuable cell source for studying human trophoblast development in pregnancy. For complete details on the use and execution of this protocol, please refer to Gao et al. (2019)1 and Ruan et al. (2022).2.

Preimplantation genetic testing for aneuploidy helps to achieve a live birth with fewer transfer cycles for the blastocyst FET patients with unexplained recurrent implantation failure.

PURPOSE: This retrospective cohort study aimed to investigate the value of preimplantation genetic testing for aneuploidy (PGT-A) as a screening test for patients suffering from unexplained recurrent implantation failure (RIF). METHODS: After screening patients in one reproductive medicine center, twenty-nine, forty-nine and thirty-eight women (< 40 years old) who had suffered unexplained RIF with PGT-A, or RIF without PGT-A, or no RIF with PGT-A were included. The clinical pregnancy rate and live birth rate per transfer, the conservative and optimal cumulative clinical pregnancy rates (CCPR) and live birth rates (CLBR) after three blastocyst FETs were analyzed. RESULTS: The live birth rate per transfer was significantly higher in the RIF + PGT-A group than that in the RIF + NO PGT-A group (47.6% vs. 24.6%, p = 0.014). After 3 cycles of FET, RIF + PGT-A group had significantly higher conservative CLBR and optimal CLBR compared to the RIF + NO PGT-A group (69.0% vs. 32.7%, p = 0.002 and 73.7% vs. 57.5%, p = 0.016), but had similar conservative and optimal CLBRs compared to the NO RIF + PGT-A group. The number of FET cycles required when half women achieved a live birth was 1 in the PGT-A group and 3 in RIF + NO PGT-A group. The miscarriage rates were not different between the RIF + PGT-A and RIF + NO PGT-A, RIF + PGT-A and NO RIF + PGT-A groups. CONCLUSION: PGT-A did be superior in reducing the number of transfer cycles required to achieve a similar live birth rate. Further studies to identify the RIF patients who would benefit most from PGT-A are necessary.

Transcriptomic analysis of mid-secretory endometrium reveals essential immune factors associated with pregnancy after single euploid blastocyst transfer.

PURPOSE: Implantation is a limiting factor for treatment success in assisted reproduction. Both embryonic and endometrial factors contribute to implantation. Embryonic factors have often been ignored in previous studies about the role of endometrium in implantation. In this study, we sought to identify the endometrial genes associated with negative pregnancy outcomes following the transfer of a single euploid blastocyst. METHODS: Computational analyses of the transcriptomes of mid-secretory endometria from nine pregnant and seven non-pregnant patients in a cycle preceding the transfer of a single euploid blastocyst in a vitrified-warmed cycle were performed. RESULTS: Principal component analysis of two reported endometrial receptivity gene sets showed close clustering of the pregnant and non-pregnant samples. Differential gene expression analysis and co-expression module analysis identified 131 genes associated with the pregnancy status. The endometrial signatures identified highlight the importance of immune and metabolic regulation in pregnancy outcome. Network analysis identified 20 hub genes that could predict pregnancy outcomes with 88.9% sensitivity and 85.7% specificity. Single-cell gene expression analysis highlighted the regulation of endometrial natural killer (NK) cells, T cells, and macrophages during embryo implantation. Immune cell abundance analysis supported the dysregulation of cytotoxic immune cells in the endometria of non-pregnant women. CONCLUSIONS: We reported the first endometrial gene signature associated with pregnancy after elimination of embryo aneuploidy and highlighted the importance of the endometrial immune microenvironment and metabolic status in pregnancy outcomes.

Metacognition and mentalizing are associated with distinct neural representations of decision uncertainty.

Metacognition and mentalizing are both associated with meta-level mental state representations. Conventionally, metacognition refers to monitoring one's own cognitive processes, while mentalizing refers to monitoring others' cognitive processes. However, this self-other dichotomy is insufficient to delineate the 2 high-level mental processes. We here used functional magnetic resonance imaging (fMRI) to systematically investigate the neural representations of different levels of decision uncertainty in monitoring different targets (the current self, the past self [PS], and others) performing a perceptual decision-making task. Our results reveal diverse formats of internal mental state representations of decision uncertainty in mentalizing, separate from the associations with external cue information. External cue information was commonly represented in the right inferior parietal lobe (IPL) across the mentalizing tasks. However, the internal mental states of decision uncertainty attributed to others were uniquely represented in the dorsomedial prefrontal cortex (dmPFC), rather than the temporoparietal junction (TPJ) that also represented the object-level mental states of decision inaccuracy attributed to others. Further, the object-level and meta-level mental states of decision uncertainty, when attributed to the PS, were represented in the precuneus and the lateral frontopolar cortex (lFPC), respectively. In contrast, the dorsal anterior cingulate cortex (dACC) represented currently experienced decision uncertainty in metacognition, and also uncertainty about the estimated decision uncertainty (estimate uncertainty), but not the estimated decision uncertainty per se in mentalizing. Hence, our findings identify neural signatures to clearly delineate metacognition and mentalizing and further imply distinct neural computations on internal mental states of decision uncertainty during metacognition and mentalizing.

Cell-Derived Extracellular Matrix Materials for Tissue Engineering.

The involvement of cell-derived extracellular matrix (CDM) in assembling tissue engineering scaffolds has yielded significant results. CDM possesses excellent characteristics, such as ideal cellular microenvironment mimicry and good biocompatibility, which make it a popular research direction in the field of bionanomaterials. CDM has significant advantages as an expansion culture substrate for stem cells, including stabilization of phenotype, reversal of senescence, and guidance of specific differentiation. In addition, the applications of CDM-assembled tissue engineering scaffolds for disease simulation and tissue organ repair are comprehensively summarized; the focus is mainly on bone and cartilage repair, skin defect or wound healing, engineered blood vessels, peripheral nerves, and periodontal tissue repair. We consider CDM as a highly promising bionic biomaterial for tissue engineering applications and propose a vision for its comprehensive development. Impact statement Cell-derived extracellular matrix (CDM) has received continued attention on the field of tissue engineering because of its promising biological characteristics. CDM deposited in vitro is rich in protein fractions and contains a wealth of biological information that provides a suitable niche for the survival and activity of isolated cells. More importantly, the free-assembling feature of CDM allows it to participate in the assembly of tissue-engineered scaffolds, imparting bionic properties to regenerative scaffolds, and thus CDM-modified scaffolds are widely used in the reconstruction of bone and cartilage tissue, peripheral nerves, skin, and blood vessels. This article is dedicated to summarizing the important results achieved by CDM-modified tissue engineering scaffolds in tissue organ reconstruction, helping readers to understand the developments in this field in recent years.

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remaining (high-risk) patients ABP is measured continuously using invasive devices, and derived values are extracted from the recorded waveforms. However, since invasive monitoring is associated with major complications (infection, bleeding, thrombosis), the ideal ABP monitor should be both non-invasive and continuous. With large volumes of high-fidelity physiological waveforms, it may be possible today to impute a physiological waveform from other available signals. Currently, the state-of-the-art approaches for ABP imputation only aim at intermittent systolic and diastolic blood pressure imputation, and there is no method that imputes the continuous ABP waveform. Here, we developed a novel approach to impute the continuous ABP waveform non-invasively using two continuously-monitored waveforms that are currently part of the standard-of-care, the electrocardiogram (ECG) and photo-plethysmogram (PPG), by adapting a deep learning architecture designed for image segmentation. Using over 150,000 min of data collected at two separate health systems from 463 patients, we demonstrate that our model provides a highly accurate prediction of the continuous ABP waveform (root mean square error 5.823 (95% CI 5.806-5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference - 2.497 ± 3.785 mmHg) compared to arterial line measurements. Our approach can potentially be used to measure blood pressure continuously and non-invasively for all patients in the acute care setting, without the need for any additional instrumentation beyond the current standard-of-care.