Heiko Schuster, Wenguang Shao, Tobias Weiss, Patrick G A Pedrioli, Patrick Roth, Michael Weller, David S Campbell, Eric W Deutsch, Robert L Moritz, Oliver Planz, Hans-Georg Rammensee, Ruedi Aebersold, Etienne Caron <<<

The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2D/K-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology. <<<

Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology. <<<

Jun Li, Melissa J Hubisz, Ethan M Earlie, Mercedes A Duran, Christy Hong, Austin A Varela, Emanuele Lettera, Matthew Deyell, Bernardo Tavora, Jonathan J Havel, Su M Phyu, Amit Dipak Amin, Karolina Budre, Erina Kamiya, Julie-Ann Cavallo, Christopher Garris, Simon Powell, Jorge S Reis-Filho, Hannah Wen, Sarah Bettigole, Atif J Khan, Benjamin Izar, Eileen E Parkes, Ashley M Laughney, Samuel F Bakhoum <<<

Chromosomal instability (CIN) is a driver of cancer metastasis, yet the extent to which this effect depends on the immune system remains unknown. Using ContactTracing-a newly developed, validated and benchmarked tool to infer the nature and conditional dependence of cell-cell interactions from single-cell transcriptomic data-we show that CIN-induced chronic activation of the cGAS-STING pathway promotes downstream signal re-wiring in cancer cells, leading to a pro-metastatic tumour microenvironment. This re-wiring is manifested by type I interferon tachyphylaxis selectively downstream of STING and a corresponding increase in cancer cell-derived endoplasmic reticulum (ER) stress response. Reversal of CIN, depletion of cancer cell STING or inhibition of ER stress response signalling abrogates CIN-dependent effects on the tumour microenvironment and suppresses metastasis in immune competent, but not severely immune compromised, settings. Treatment with STING inhibitors reduces CIN-driven metastasis in melanoma, breast and colorectal cancers in a manner dependent on tumour cell-intrinsic STING. Finally, we show that CIN and pervasive cGAS activation in micronuclei are associated with ER stress signalling, immune suppression and metastasis in human triple-negative breast cancer, highlighting a viable strategy to identify and therapeutically intervene in tumours spurred by CIN-induced inflammation. <<<

Ana Luísa Correia, Joao C Guimaraes, Priska Auf der Maur, Duvini De Silva, Marcel P Trefny, Ryoko Okamoto, Sandro Bruno, Alexander Schmidt, Kirsten Mertz, Katrin Volkmann, Luigi Terracciano, Alfred Zippelius, Marcus Vetter, Christian Kurzeder, Walter Paul Weber, Mohamed Bentires-Alj <<<

The persistence of undetectable disseminated tumour cells (DTCs) after primary tumour resection poses a major challenge to effective cancer treatment. These enduring dormant DTCs are seeds of future metastases, and the mechanisms that switch them from dormancy to outgrowth require definition. Because cancer dormancy provides a unique therapeutic window for preventing metastatic disease, a comprehensive understanding of the distribution, composition and dynamics of reservoirs of dormant DTCs is imperative. Here we show that different tissue-specific microenvironments restrain or allow the progression of breast cancer in the liver-a frequent site of metastasis that is often associated with a poor prognosis. Using mouse models, we show that there is a selective increase in natural killer (NK) cells in the dormant milieu. Adjuvant interleukin-15-based immunotherapy ensures an abundant pool of NK cells that sustains dormancy through interferon-γ signalling, thereby preventing hepatic metastases and prolonging survival. Exit from dormancy follows a marked contraction of the NK cell compartment and the concurrent accumulation of activated hepatic stellate cells (aHSCs). Our proteomics studies on liver co-cultures implicate the aHSC-secreted chemokine CXCL12 in the induction of NK cell quiescence through its cognate receptor CXCR4. CXCL12 expression and aHSC abundance are closely correlated in patients with liver metastases. Our data identify the interplay between NK cells and aHSCs as a master switch of cancer dormancy, and suggest that therapies aimed at normalizing the NK cell pool might succeed in preventing metastatic outgrowth. <<<

Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into the prediction process. The problem is challenging, however, as it requires both making predictions with arbitrary feature sets and learning a policy to identify the most valuable selections. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. The main challenge is learning this selection policy, and we design a straightforward new modeling approach that estimates the mutual information in a discriminative rather than generative fashion. Building on our learning approach, we introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform costs between features, incorporating prior information, and exploring modern architectures to handle partial input information. We find that our method provides consistent gains over recent state-of-the-art methods across a variety of datasets. <<<

Knowledge of the human microbiome, which is likely a critical factor in the initiation, progression, and prognosis of multiple forms of cancer, is rapidly expanding. In this review, we focus on recent investigations to discern putative, causative microbial species and the microbiome composition and structure currently associated with procarcinogenesis and tumorigenesis at select body sites. We specifically highlight forms of cancer, gastrointestinal and nongastrointestinal, that have significant bacterial associations and well-defined experimental evidence with the aim of generating directions for future experimental and translational investigations to develop a clearer understanding of the multifaceted mechanisms by which microbiota affect cancer formation. SIGNIFICANCE: Emerging and, for some cancers, strong experimental and translational data support the contribution of the microbiome to cancer biology and disease progression. Disrupting microbiome features and pathways contributing to cancer may provide new approaches to improving cancer outcomes in patients. <<<

Hong Yang , Xiao Chen , Zuo-Bing Chen , Le Li , Xue-Ying Li , Francisco Xavier Castellanos , Tong-Jian Bai , Qi-Jing Bo , Jun Cao , Zhi-Kai Chang , Guan-Mao Chen , Ning-Xuan Chen , Wei Chen , Chang Cheng , Yu-Qi Cheng , Xi-Long Cui , Jia Duan , Yiru Fang , Qi-Yong Gong , Wen-Bin Guo , Zheng-Hua Hou , Lan Hu , Li Kuang , Feng Li , Hui-Xian Li , Kai-Ming Li , Tao Li , Yan-Song Liu , Zhe-Ning Liu , Yi-Cheng Long , Bin Lu , Qing-Hua Luo , Hua-Qing Meng , Daihui Peng , Hai-Tang Qiu , Jiang Qiu , Yue-Di Shen , Yu-Shu Shi , Tian-Mei Si , Yan-Qing Tang , Chuan-Yue Wang , Fei Wang , Kai Wang , Li Wang , Xiang Wang , Ying Wang , Yu-Wei Wang , Xiao-Ping Wu , Xin-Ran Wu , Chun-Ming Xie , Guang-Rong Xie , Hai-Yan Xie , Peng Xie , Xiu-Feng Xu , Jian Yang , Jia-Shu Yao , Shu-Qiao Yao , Ying-Ying Yin , Yong-Gui Yuan , Yu-Feng Zang , Ai-Xia Zhang , Hong Zhang , Ke-Rang Zhang , Lei Zhang , Zhi-Jun Zhang , Jing-Ping Zhao , Rubai Zhou , Yi-Ting Zhou , Jun-Juan Zhu , Zhi-Chen Zhu , Chao-Jie Zou , Xi-Nian Zuo , Chao-Gan Yan <<<

AbstractAberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks. <<<

Events associated with aversive or rewarding outcomes are prioritized in memory. This memory boost is commonly attributed to the elicited affective response, closely linked to noradrenergic and dopaminergic modulation of hippocampal plasticity. Herein we review and compare this 'affect' mechanism to an additional, recently discovered, 'prediction' mechanism whereby memories are strengthened by the extent to which outcomes deviate from expectations, that is, by prediction errors (PEs). The mnemonic impact of PEs is separate from the affective outcome itself and has a distinct neural signature. While both routes enhance memory, these mechanisms are linked to different - and sometimes opposing - predictions for memory integration. We discuss new findings that highlight mechanisms by which emotional events strengthen, integrate, and segment memory. <<<

AbstractThe infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requiresa prioriknowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 112 babies from 6 to 98 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain. <<<

Noninvasively mapping the layout of cortical areas in humans is a continuing challenge for neuroscience. We present a new method of mapping cortical areas based on myelin content as revealed by T1-weighted (T1w) and T2-weighted (T2w) MRI. The method is generalizable across different 3T scanners and pulse sequences. We use the ratio of T1w/T2w image intensities to eliminate the MR-related image intensity bias and enhance the contrast to noise ratio for myelin. Data from each subject were mapped to the cortical surface and aligned across individuals using surface-based registration. The spatial gradient of the group average myelin map provides an observer-independent measure of sharp transitions in myelin content across the surface--i.e., putative cortical areal borders. We found excellent agreement between the gradients of the myelin maps and the gradients of published probabilistic cytoarchitectonically defined cortical areas that were registered to the same surface-based atlas. For other cortical regions, we used published anatomical and functional information to make putative identifications of dozens of cortical areas or candidate areas. In general, primary and early unimodal association cortices are heavily myelinated and higher, multimodal, association cortices are more lightly myelinated, but there are notable exceptions in the literature that are confirmed by our results. The overall pattern in the myelin maps also has important correlations with the developmental onset of subcortical white matter myelination, evolutionary cortical areal expansion in humans compared with macaques, postnatal cortical expansion in humans, and maps of neuronal density in non-human primates. <<<

Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality. The code and datasets are available at this https URL. <<<

Guangxi Wang, Mantang Qiu, Xudong Xing, Juntuo Zhou, Hantao Yao, Mingru Li, Rong Yin, Yan Hou, Yang Li, Shuli Pan, Yuqing Huang, Fan Yang, Fan Bai, Honggang Nie, Shuangshuang Di, Limei Guo, Zhu Meng, Jun Wang, Yuxin Yin <<<

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention. <<<

Kevin B Einkauf, Matthew R Osborn, Ce Gao, Weiwei Sun, Xiaoming Sun, Xiaodong Lian, Elizabeth M Parsons, Gregory T Gladkov, Kyra W Seiger, Jane E Blackmer, Chenyang Jiang, Steven A Yukl, Eric S Rosenberg, Xu G Yu, Mathias Lichterfeld <<<

HIV-1-infected cells that persist despite antiretroviral therapy (ART) are frequently considered "transcriptionally silent," but active viral gene expression may occur in some cells, challenging the concept of viral latency. Applying an assay for profiling the transcriptional activity and the chromosomal locations of individual proviruses, we describe a global genomic and epigenetic map of transcriptionally active and silent proviral species and evaluate their longitudinal evolution in persons receiving suppressive ART. Using genome-wide epigenetic reference data, we show that proviral transcriptional activity is associated with activating epigenetic chromatin features in linear proximity of integration sites and in their inter- and intrachromosomal contact regions. Transcriptionally active proviruses were actively selected against during prolonged ART; however, this pattern was violated by large clones of virally infected cells that may outcompete negative selection forces through elevated intrinsic proliferative activity. Our results suggest that transcriptionally active proviruses are dynamically evolving under selection pressure by host factors. <<<

Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Abubakr Babiker, Nathanael Schärli, Aakanksha Chowdhery, Philip Mansfield, Dina Demner-Fushman, Blaise Agüera Y Arcas, Dale Webster, Greg S Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle Barral, Christopher Semturs, Alan Karthikesalingam, Vivek Natarajan <<<

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA and Measuring Massive Multitask Language Understanding (MMLU) clinical topics), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications. <<<

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging. <<<

Jianqing Niu, Shengwei Ma, Shusong Zheng, Chi Zhang, Yaru Lu, Yaoqi Si, Shuiquan Tian, Xiaoli Shi, Xiaolin Liu, Muhammad Kashif Naeem, Hua Sun, Yafei Hu, Huilan Wu, Yan Cui, Chunlin Chen, Wenbo Long, Yue Zhang, Mengjun Gu, Man Cui, Qiao Lu, Wenjuan Zhou, Junhua Peng, Eduard Akhunov, Fei He, Shancen Zhao, Hong-Qing Ling <<<

Breeding has dramatically changed the plant architecture of wheat (Triticum aestivum), resulting in the development of high-yielding varieties adapted to modern farming systems. However, how wheat breeding shaped the genomic architecture of this crop remains poorly understood. Here, we performed a comprehensive comparative analysis of a whole-genome resequencing panel of 355 common wheat accessions (representing diverse landraces and modern cultivars from China and the United States) at the phenotypic and genomic levels. The genetic diversity of modern wheat cultivars was clearly reduced compared to landraces. Consistent with these genetic changes, most phenotypes of cultivars from China and the United States were significantly altered. Of the 21 agronomic traits investigated, 8 showed convergent changes between the 2 countries. Moreover, of the 207 loci associated with these 21 traits, more than half overlapped with genomic regions that showed evidence of selection. The distribution of selected loci between the Chinese and American cultivars suggests that breeding for increased productivity in these 2 regions was accomplished by pyramiding both shared and region-specific variants. This work provides a framework to understand the genetic architecture of the adaptation of wheat to diverse agricultural production environments, as well as guidelines for optimizing breeding strategies to design better wheat varieties. <<<

Kimberly Martin, Pe'er Dar, Cora MacPherson, Melissa Egbert, Zachary Demko, Sheetal Parmar, Katelyn Hashimoto, Sina Haeri, Fergal Malone, Ronald J Wapner, Ashley S Roman, Asma Khalil, Revital Faro, Rajeevi Madankumar, Noel Strong, Robert M Silver, Nidhi Vohra, Jon Hyett, Matt Rabinowitz, Charlly Kao, Hakon Hakonarson, Bo Jacobsson, Mary E Norton <<<

PURPOSE: The aim of this study was to assess the performance of cell-free DNA (cfDNA) screening to detect sex chromosome aneuploidies (SCAs) in an unselected obstetrical population with genetic confirmation.METHODS: This was a planned secondary analysis of the multicenter, prospective SNP-based Microdeletion and Aneuploidy RegisTry (SMART) study. Patients receiving cfDNA results for autosomal aneuploidies and who had confirmatory genetic results for the relevant sex chromosomal aneuploidies were included. Screening performance for SCAs, including monosomy X (MX) and the sex chromosome trisomies (SCT: 47,XXX; 47,XXY; 47,XYY) was determined. Fetal sex concordance between cfDNA and genetic screening was also evaluated in euploid pregnancies.RESULTS: A total of 17,538 cases met inclusion criteria. Performance of cfDNA for MX, SCTs, and fetal sex was determined in 17,297, 10,333, and 14,486 pregnancies, respectively. Sensitivity, specificity, and positive predictive value (PPV) of cfDNA were 83.3%, 99.9%, and 22.7% for MX and 70.4%, 99.9%, and 82.6%, respectively, for the combined SCTs. The accuracy of fetal sex prediction by cfDNA was 100%.CONCLUSION: Screening performance of cfDNA for SCAs is comparable to that reported in other studies. The PPV for the SCTs was similar to the autosomal trisomies, whereas the PPV for MX was substantially lower. No discordance in fetal sex was observed between cfDNA and postnatal genetic screening in euploid pregnancies. These data will assist interpretation and counseling for cfDNA results for sex chromosomes. <<<

Avishai Gavish, Michael Tyler, Alissa C Greenwald, Rouven Hoefflin, Dor Simkin, Roi Tschernichovsky, Noam Galili Darnell, Einav Somech, Chaya Barbolin, Tomer Antman, Daniel Kovarsky, Thomas Barrett, L Nicolas Gonzalez Castro, Debdatta Halder, Rony Chanoch-Myers, Julie Laffy, Michael Mints, Adi Wider, Rotem Tal, Avishay Spitzer, Toshiro Hara, Maria Raitses-Gurevich, Chani Stossel, Talia Golan, Amit Tirosh, Mario L Suvà, Sidharth V Puram, Itay Tirosh <<<

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH. <<<

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications. <<<

: Cancer therapy have evolved remarkably over the past decade, providing new strategies to inhibit cancer cell growth using immune modulation, with or without gene therapy. Specifically, suicide gene therapies and immunotoxins have been investigated for the treatment of tumors by direct cancer cell cytotoxicity. Recent advances in mRNA delivery also demonstrated the potential of mRNA-based vaccines and immune-modulators for cancer therapeutics by utilizing nanocarriers for mRNA delivery. We designed a bacterial toxin-encoding modified mRNA, delivered by lipid nanoparticles into a B16-melanoma mouse model. : We showed that local administration of LNPs entrapping a modified mRNA that encodes for a bacterial toxin, induced significant anti-tumor effects and improved overall survival of treated mice. We propose mmRNA-loaded LNPs as a new class of anti-tumoral, toxin-based therapy. <<<