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"Li, Da"
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DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.
The analysis of NMR spectra of complex biochemical samples with respect to individual resonances is challenging but critically important. Here, the authors present a deep learning-based method that accelerates this process also for crowded NMR data that are non-trivial to analyze, even by expert NMR spectroscopists.
Journal Article
Crosstalk Between Trophoblasts and Decidual Immune Cells: The Cornerstone of Maternal-Fetal Immunotolerance
The success of pregnancy relies on the fine adjustment of the maternal immune system to tolerate the allogeneic fetus. Trophoblasts carrying paternal antigens are the only fetal-derived cells that come into direct contact with the maternal immune cells at the maternal–fetal interface. The crosstalk between trophoblasts and decidual immune cells (DICs) via cell–cell direct interaction and soluble factors such as chemokines and cytokines is a core event contributing to the unique immunotolerant microenvironment. Abnormal trophoblasts–DICs crosstalk can lead to dysregulated immune situations, which is well known to be a potential cause of a series of pregnancy complications including recurrent spontaneous abortion (RSA), which is the most common one. Immunotherapy has been applied to RSA. However, its development has been far less rapid or mature than that of cancer immunotherapy. Elucidating the mechanism of maternal–fetal immune tolerance, the theoretical basis for RSA immunotherapy, not only helps to understand the establishment and maintenance of normal pregnancy but also provides new therapeutic strategies and promotes the progress of immunotherapy against pregnancy-related diseases caused by disrupted immunotolerance. In this review, we focus on recent progress in the maternal–fetal immune tolerance mediated by trophoblasts–DICs crosstalk and clinical application of immunotherapy in RSA. Advancement in this area will further accelerate the basic research and clinical transformation of reproductive immunity and tumor immunity.
Journal Article
Comprehensive metabolomics expands precision medicine for triple-negative breast cancer
Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal androgen receptor (LAR) subtype overlapped with metabolomic C1 subtype. Experiments on patient-derived organoid and xenograft models indicate that targeting sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, is a promising therapy for LAR tumors. Moreover, the transcriptomic basal-like immune-suppressed (BLIS) subtype contained two prognostic metabolomic subgroups (C2 and C3), which could be distinguished through machine-learning methods. We show that N-acetyl-aspartyl-glutamate is a crucial tumor-promoting metabolite and potential therapeutic target for high-risk BLIS tumors. Together, our study reveals the clinical significance of TNBC metabolomics, which can not only optimize the transcriptomic subtyping system, but also suggest novel therapeutic targets. This metabolomic dataset can serve as a useful public resource to promote precision treatment of TNBC.
Journal Article
Experimental quantum repeater without quantum memory
Quantum repeaters—important components of a scalable quantum internet—enable entanglement to be distributed over long distances. The standard paradigm for a quantum repeater relies on the necessary, demanding requirement of quantum memory. Despite significant progress, the limited performance of quantum memory means that making practical quantum repeaters remains a challenge. Remarkably, a proposed all-photonic quantum repeater avoids the need for quantum memory by harnessing the graph states in the repeater nodes. Here we perform an experimental demonstration of an all-photonic quantum repeater. By manipulating a 12-photon interferometer, we implement a 2 × 2 parallel all-photonic quantum repeater, and observe an 89% enhancement of entanglement-generation rate over standard parallel entanglement swapping. These results provide a new approach to designing repeaters with efficient single-photon sources and photonic graph states, and suggest that the all-photonic scheme represents an alternative path—parallel to matter-memory-based schemes—towards realizing practical quantum repeaters.
Journal Article
شيطان بتسعة رؤوس
وصل سان تسانغ وتلاميذه إلى قرية توهلوه حيث واجهوا شيطانا ضخما على هيئة ثعبان يأكل الماشية والأغنام والدجاج والإوز وحتى البشر. طلب سكان القرية المساعدة من السحرة لكن الشيطان قتلهم. ولاحقا، ذهب القرد الإخضاع الشيطان، وبمجرد أن فتح الثعبان فمه ليأكل با جیه قفز القرد داخل معدته وقتله. فرح أهل القرية بمقتل الشيطان الثعبان ورافقوا سان تسانغ وتلاميذه لتوديعهم. وكان في غرب القرية جبل الهيات السبع، وبسبب تراكم ثمار الكاكي لعدة سنوات، صار الجبل قذرا كريه الرائحة، وعجز الأربعة عن عبوره. فعمل با جيه ليلا ونهارا لتنظيف الطريق بعد أن أطعمه أهل القرية ما يكفي حتى تضاعف وصار ضخما جدا. وأخيرا، عبر الأربعة الجبل.
Book
Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma
Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. To comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, we employed single-cell RNA-seq (scRNA-seq) to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases, and identified diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, in PDAC. We found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, we found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, our findings provide a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.
Journal Article