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Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell , for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions' input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable with the one obtained with the centralized non-secure solution. PriCell guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data., Competing Interests: J.R.T.-P. and J.-P.H. are co-founders of the start-up Tune Insight SA (https://tuneinsight.com). The other authors declare no competing interests. The methods presented in this work are partially covered by the PCT patent applications under publication nos. WO/2022/04284848 and WO/2021/223873.49, (© 2022 The Authors.)

Ample evidence pinpoints the phenotypic diversity of blood vessels (BVs) and site-specific functions of their lining endothelial cells (ECs). We harnessed single-cell RNA sequencing (scRNA-seq) to dissect the molecular heterogeneity of blood vascular endothelial cells (BECs) in healthy adult human skin and identified six different subpopulations, signifying arterioles, post-arterial capillaries, pre-venular capillaries, post-capillary venules, venules and collecting venules. Individual BEC subtypes exhibited distinctive transcriptomic landscapes associated with diverse biological pathways. These functionally distinct dermal BV segments were characterized by their unique compositions of conventional and novel markers (e.g., arteriole marker GJA5; arteriole capillary markers ASS1 and S100A4; pre-venular capillary markers SOX17 and PLAUR; venular markers EGR2 and LRG1), many of which have been implicated in vascular remodeling upon inflammatory responses. Immunofluorescence staining of human skin sections and whole-mount skin blocks confirmed the discrete expression of these markers along the blood vascular tree in situ, further corroborating BEC heterogeneity in human skin. Overall, our study molecularly refines individual BV compartments, whilst the identification of novel subtype-specific signatures provides more insights for future studies dissecting the responses of distinct vessel segments under pathological conditions.

Single-cell RNA sequencing (scRNA-seq) approaches have transformed our ability to resolve cellular properties across systems, but are currently tailored toward large cell inputs (>1,000 cells). This renders them inefficient and costly when processing small, individual tissue samples, a problem that tends to be resolved by loading bulk samples, yielding confounded mosaic cell population read-outs. Here, we developed a deterministic, mRNA-capture bead and cell co-encapsulation dropleting system, DisCo, aimed at processing low-input samples (<500 cells). We demonstrate that DisCo enables precise particle and cell positioning and droplet sorting control through combined machine-vision and multilayer microfluidics, enabling continuous processing of low-input single-cell suspensions at high capture efficiency (>70%) and at speeds up to 350 cells per hour. To underscore DisCo's unique capabilities, we analyzed 31 individual intestinal organoids at varying developmental stages. This revealed extensive organoid heterogeneity, identifying distinct subtypes including a regenerative fetal-like Ly6a + stem cell population that persists as symmetrical cysts, or spheroids, even under differentiation conditions, and an uncharacterized 'gobloid' subtype consisting predominantly of precursor and mature (Muc2 + ) goblet cells. To complement this dataset and to demonstrate DisCo's capacity to process low-input, in vivo-derived tissues, we also analyzed individual mouse intestinal crypts. This revealed the existence of crypts with a compositional similarity to spheroids, which consisted predominantly of regenerative stem cells, suggesting the existence of regenerating crypts in the homeostatic intestine. These findings demonstrate the unique power of DisCo in providing high-resolution snapshots of cellular heterogeneity in small, individual tissues., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Objectives: To determine the prevalence and types of viral pathogens in the myocardium of patients presenting with clinically suspected myocarditis in South Africa., Method: This is a prospective cross-sectional study. Consecutive adults presenting to a single tertiary centre in South Africa between August 2017 and January 2021 who fulfilled the European Society of Cardiology's diagnostic criteria for clinically suspected myocarditis and who had undergone the appropriate investigations, including cardiac MRI (CMR) and endomyocardial biopsy (EMB), were included., Results: One hundred and two patients with clinically suspected myocarditis were enrolled. Acute myocarditis (AM) was confirmed by CMR or EMB in 82 (80.39%) patients. Viral genomes were detected by PCR in EMB specimens of 50 patients with AM. Parvovirus B19 (PVB19) was the most frequently detected virus, in 37 as monoinfection and 4 as coinfection. This was followed by Epstein-Barr virus (n=6), human herpesvirus 6 (n=2) and human bocavirus (n=1). PVB19 was also detected in 9 patients with no evidence of AM on CMR or EMB., Conclusion: Viral myocarditis is the most common form of myocarditis in South Africa. Local viral prevalence appears to be similar those of the developed world. The clinical significance and pathogenic role of PVB19 remains questioned, and its local background prevalence will have to be further investigated., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

Recently, the twist angle between adjacent sheets of stacked van der Waals materials emerged as a new knob to engineer correlated states of matter in two-dimensional heterostructures in a controlled manner, giving rise to emergent phenomena such as superconductivity or correlated insulating states. Here, we use an ab initio based approach to characterize the electronic properties of twisted bilayer MoS 2 . We report that, in marked contrast to twisted bilayer graphene, slightly hole-doped MoS 2 realizes a strongly asymmetric p x -p y Hubbard model on the honeycomb lattice, with two almost entirely dispersionless bands emerging due to destructive interference. The origin of these dispersionless bands, is similar to that of the flat bands in the prototypical Lieb or Kagome lattices and co-exists with the general band flattening at small twist angle due to the moiré interference. We study the collective behavior of twisted bilayer MoS 2 in the presence of interactions, and characterize an array of different magnetic and orbitally-ordered correlated phases, which may be susceptible to quantum fluctuations giving rise to exotic, purely quantum, states of matter., (© 2021. The Author(s).)

Twisting two adjacent layers of van der Waals materials with respect to each other can lead to flat two-dimensional electronic bands which enables a wealth of physical phenomena. Here, we generalize this concept of so-called moiré flat bands to engineer flat bands in all three spatial dimensions controlled by the twist angle. The basic concept is to stack the material such that the large spatial moiré interference patterns are spatially shifted from one twisted layer to the next. We exemplify the general concept by considering graphitic systems, boron nitride, and WSe 2 , but the approach is applicable to any two-dimensional van der Waals material. For hexagonal boron nitride, we develop an ab initio fitted tight binding model that captures the corresponding three-dimensional low-energy electronic structure. We outline that interesting three-dimensional correlated phases of matter can be induced and controlled following this route, including quantum magnets and unconventional superconducting states.

Intrahepatic cholangiocarcinoma (iCCA) has emerged as a promising candidate for precision medicine, especially in the case of activating FGFR2 gene fusions. In addition to fusions, a considerable fraction of iCCA patients reveals FGFR2 mutations, which might lead to uncontrolled activation of the FGFR2 pathway but are mostly of unknown functional significance. A current challenge for molecular tumor boards (MTB) is to predict the functional consequences of such FGFR2 alterations to guide potential treatment decisions. We report two iCCA patients with extracellular and juxtamembrane FGFR2 mutations. After in silico investigation of the alterations and identification of activated FGFR2 downstream targets in tumor specimens by immunohistochemistry and transcriptome analysis, the MTB recommended treatment with an FGFR-inhibiting tyrosine kinase inhibitor. Both patients developed a rapidly detectable and prolonged partial response to treatment. These two cases suggest an approach to characterize further detected FGFR2 mutations in iCCA to enable patients´ selection for a successful application of the FGFR -inhibiting drugs., (© 2021. The Author(s).)

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Women living with HIV (WLWH) experience high rates of anal cancer. Screening using anal cytology, high-resolution anoscopy (HRA) with biopsies, can histologically diagnose anal cancer precursors called high-grade squamous intraepithelial lesions (HSIL). The low specificity of screening using anal cytology results in HRA referral for many WLWH without HSIL. Screening using high-risk human papillomavirus (HR-HPV) may improve specificity., Methods: Two hundred seven WLWH (63% non-Hispanic black) were screened for anal histologic HSIL (hHSIL) using cytology, HRA-guided biopsies, and Xpert HPV. Xpert performance for predicting anal hHSIL was compared with that of cytology. Usng Xpert 5 HPV genotypic results and accompanying cycle thresholds, receiver operator characteristic curve and recursive partitioning analyses were used to create predictive models for hHSIL., Results: The performance of Xpert to predict hHSIL was not different from that of cytology with a sensitivity (Sn) of 89% and specificity (Sp) of 49%. Interpretation of Xpert was modified using genotypic results and receiver operator characteristic curve analysis, which produced a screen with an Sn and Sp of 75% and 84% for hHSIL, respectively. Another reinterpretation of Xpert was created using recursive partitioning and cycle thresholds, which predicted hHSIL with an Sn and Sp of 75% and 86%, respectively. The detection of HPV-16 was highly predictive of hHSIL in all analyses. These modified screening tests would reduce HRA referral in this population by almost half compared with anal cytology., Conclusions: Xpert HPV is an alternative to anal cytology to screen for anal HSIL and can be optimized to reduce the number of unnecessary HRAs performed in WLWH., Competing Interests: G.B.E. was supported by T32 AI007613 (Division of Infectious Weill Cornell Medicine, R. Gulick) and UL1 TR002384 (Weill Cornell Medicine Clinical and Translational Science Center). Funding for this work came from UM1 CA121947 (AIDS Malignancy Consortium, R. Mitsuyasu) and R01 CA163103 (E.Y.C.). N.J. has received honoraria from Antiva and is on Merck and Coadvisory board. R.D.C. has received personal fees from UpToDate, outside the submitted work. S.E.G. has received personal fees from Merck and Co, grants from Antiva and Inovio, and other support from THD America. T.D. has received nonfinancial support from Hologic and personal fees from Roche, BD, Antiva, and TheVax. M.H.E. has advised or participated in educational speaking activities but does not receive honoraria from any companies; his employers have received payment for his time spent for these activities from Papivax, Cynvec, Altum Pharma, Photocure, Becton Dickinson, and PDS Biotechnologies. Rutgers has received grant funding for research-related costs of clinical trials on which M.H.E. has been the overall or local principal investigator from Johnson & Johnson, Pfizer, AstraZeneca, Advaxis, and Inovio; he has also received other support from Photocure, Papivax, Cynvec, PDS, Altum Pharma, and Becton Dickinson, outside the submitted work. E.A.S. has received nonfinancial support from Qiagen and Hologic. J.M.B.-L. has received personal fees from Antiva. J.M.P. has received grants and nonfinancial support from Merck and Co; grants, personal fees, and other support from Vir Biotechnologies, Ubiome, and Antiva Biosciences; personal fees from Janssen Pharmaceuticals, Novan, and Vaccitech; and nonfinancial support from Virion Therapeutics. T.J.W. has received grants and personal fees from GlaxoSmithKline/ViiV Healthcare, outside the submitted work. The remaining authors have no conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods., Competing Interests: The authors have declared that no competing interests exist.

Background: Children seem relatively protected from serious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related disease, but little is known about children living in settings with high tuberculosis and human immunodeficiency virus (HIV) burden. This study reflects clinical data on South African children with SARS-CoV-2., Methods: We collected clinical data of children aged <13 years with laboratory-confirmed SARS-CoV-2 presenting to Tygerberg Hospital, Cape Town, between 17 April and 24 July 2020., Results: One hundred fifty-nine children (median age, 48.0 months [interquartile range {IQR}, 12.0-106.0 months]) were included. Hospitalized children (n = 62), with a median age of 13.5 months (IQR, 1.8-43.5 months) were younger than children not admitted (n = 97; median age, 81.0 months [IQR, 34.5-120.5 months]; P < .01.). Thirty-three of 159 (20.8%) children had preexisting medical conditions. Fifty-one of 62 (82.3%) hospitalized children were symptomatic; lower respiratory tract infection was diagnosed in 21 of 51 (41.2%) children, and in 11 of 16 (68.8%) children <3 months of age. Respiratory support was required in 25 of 51 (49.0%) children; 13 of these (52.0%) were <3 months of age. One child was HIV infected and 11 of 51 (21.2%) were HIV exposed but uninfected, and 7 of 51 (13.7%) children had a recent or new diagnosis of tuberculosis., Conclusions: Children <1 year of age hospitalized with SARS-CoV-2 in Cape Town frequently required respiratory support. Access to oxygen may be limited in some low- and middle-income countries, which could potentially drive morbidity and mortality. HIV infection was uncommon but a relationship between HIV exposure, tuberculosis, and SARS-CoV-2 should be explored., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: Enteroviruses are amongst the most common causes of aseptic meningitis. Between November 2018 and May 2019, an outbreak of enterovirus-associated aseptic meningitis cases was noted in the Western and Eastern Cape Provinces, South Africa., Objectives: To describe the epidemiology and phylogeography of enterovirus infections during an aseptic meningitis outbreak in the Western and Eastern Cape Provinces of South Africa., Methods: Cerebrospinal fluid samples from suspected cases were screened using a polymerase chain reaction targeting the 5'UTR. Confirmed enterovirus-associated meningitis samples underwent molecular typing through species-specific VP1/VP2 primers and pan-species VP1 primers., Results: Between November 2018 and May 2019, 3497 suspected cases of aseptic meningitis were documented in the Western and Eastern Cape Provinces. Median age was 8 years (range 0-61), interquartile range (IQR=4-13 years), 405/735 (55%) male. 742/3497 (21%) cases were laboratory - confirmed enterovirus positive by routine diagnostic PCR targeting the 5'UTR. 128/742 (17%) underwent molecular typing by VP1 gene sequencing. Echovirus 4 (E4) was detected in 102/128 (80%) cases. Echovirus 9 was found in 7%, Coxsackievirus A13 in 3%. 10 genotypes contributed to the remaining 10% of cases. Synonymous mutations were found in most cases, with sporadic amino acid changes in 13 (12.7%) cases., Conclusion: The aseptic meningitis outbreak was associated with echovirus 4. Stool samples are valuable for molecular typing in CSF confirmed EV-associated aseptic meningitis., (Copyright © 2021. Published by Elsevier B.V.)

Hepatocellular carcinoma (HCC) can have viral or non-viral causes 1-5 . Non-alcoholic steatohepatitis (NASH) is an important driver of HCC. Immunotherapy has been approved for treating HCC, but biomarker-based stratification of patients for optimal response to therapy is an unmet need 6,7 . Here we report the progressive accumulation of exhausted, unconventionally activated CD8 + PD1 + T cells in NASH-affected livers. In preclinical models of NASH-induced HCC, therapeutic immunotherapy targeted at programmed death-1 (PD1) expanded activated CD8 + PD1 + T cells within tumours but did not lead to tumour regression, which indicates that tumour immune surveillance was impaired. When given prophylactically, anti-PD1 treatment led to an increase in the incidence of NASH-HCC and in the number and size of tumour nodules, which correlated with increased hepatic CD8 + PD1 + CXCR6 + , TOX + , and TNF + T cells. The increase in HCC triggered by anti-PD1 treatment was prevented by depletion of CD8 + T cells or TNF neutralization, suggesting that CD8 + T cells help to induce NASH-HCC, rather than invigorating or executing immune surveillance. We found similar phenotypic and functional profiles in hepatic CD8 + PD1 + T cells from humans with NAFLD or NASH. A meta-analysis of three randomized phase III clinical trials that tested inhibitors of PDL1 (programmed death-ligand 1) or PD1 in more than 1,600 patients with advanced HCC revealed that immune therapy did not improve survival in patients with non-viral HCC. In two additional cohorts, patients with NASH-driven HCC who received anti-PD1 or anti-PDL1 treatment showed reduced overall survival compared to patients with other aetiologies. Collectively, these data show that non-viral HCC, and particularly NASH-HCC, might be less responsive to immunotherapy, probably owing to NASH-related aberrant T cell activation causing tissue damage that leads to impaired immune surveillance. Our data provide a rationale for stratification of patients with HCC according to underlying aetiology in studies of immunotherapy as a primary or adjuvant treatment.

Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities., (© 2021 The Authors.)

Background: The impressive progress in the field of stem cell research in the past decades has provided the ground for the development of cell-based therapy. Mesenchymal stromal cells obtained from adipose tissue (AD-MSCs) represent a viable source for the development of cell-based therapies. However, the heterogeneity and variable differentiation ability of AD-MSCs depend on the cellular composition and represent a strong limitation for their use in therapeutic applications. In order to fully understand the cellular composition of MSC preparations, it would be essential to analyze AD-MSCs at single-cell level., Method: Recent advances in single-cell technologies have opened the way for high-dimensional, high-throughput, and high-resolution measurements of biological systems. We made use of the cytometry by time-of-flight (CyTOF) technology to explore the cellular composition of 17 human AD-MSCs, interrogating 31 markers at single-cell level. Subcellular composition of the AD-MSCs was investigated in their naïve state as well as during osteogenic commitment, via unsupervised dimensionality reduction as well as supervised representation learning approaches., Result: This study showed a high heterogeneity and variability in the subcellular composition of AD-MSCs upon isolation and prolonged culture. Algorithm-guided identification of emerging subpopulations during osteogenic differentiation of AD-MSCs allowed the identification of an ALP+/CD73+ subpopulation of cells with enhanced osteogenic differentiation potential. We could demonstrate in vitro that the sorted ALP+/CD73+ subpopulation exhibited enhanced osteogenic potential and is moreover fundamental for osteogenic lineage commitment. We finally showed that this subpopulation was present in freshly isolated human adipose-derived stromal vascular fractions (SVFs) and that could ultimately be used for cell therapies., Conclusion: The data obtained reveal, at single-cell level, the heterogeneity of AD-MSCs from several donors and highlight how cellular composition impacts the osteogenic differentiation capacity. The marker combination (ALP/CD73) can not only be used to assess the differentiation potential of undifferentiated AD-MSC preparations, but also could be employed to prospectively enrich AD-MSCs from the stromal vascular fraction of human adipose tissue for therapeutic applications.

Purpose: To evaluate the effects of topical castor oil application to the eyelids on ocular surface and tear film parameters in patients with blepharitis., Methods: Twenty-six participants (14 females, 12 males; mean ± SD age, 38 ± 21 years) with clinical signs of blepharitis were enrolled in a prospective, investigator-masked, randomized, paired-eye trial. A 100% cold pressed castor oil formulation (Lotus Garden Botanicals, Biddeford, ME, USA) was applied to the eyelids of one eye (randomized), twice daily for 4 weeks. Ocular surface characteristics, symptoms, and tear film parameters were assessed at baseline and day 28., Results: Baseline measurements did not differ between treated and control eyes (all p > 0.05). A significant reduction in OSDI symptomology score was observed following the four-week treatment period (p = 0.001). Clinical improvements in eyelid margin thickening, telangiectasia, eyelash matting, madarosis, cylindrical dandruff, and lid wiper epitheliopathy were limited to treated eyes (all p < 0.01), while greater decreases in staphylococcal and seborrheic eyelash crusting were observed in treated than control eyes (both p < 0.05). No adverse events were reported during the treatment period., Conclusion: Topical castor oil application effected significant improvements in ocular surface signs and symptoms in patients with blepharitis. The favourable therapeutic profile would suggest that castor oil demonstrates promise as a potential treatment for blepharitis, and support the conduct of further efficacy trials with longer follow up., Trial Registration Number: ACTRN12618000856213., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Glioblastoma is an invariably deadly disease. A subpopulation of glioma stem-like cells (GSCs) drives tumor progression and treatment resistance. Two recent studies demonstrated that neurons form oncogenic glutamatergic electrochemical synapses with post-synaptic GSCs. This led us to explore whether glutamate signaling through G protein-coupled metabotropic receptors would also contribute to the malignancy of glioblastoma. We found that glutamate metabotropic receptor (Grm)3 is the predominantly expressed Grm in glioblastoma. Associations of GRM3 gene expression levels with survival are confined to the proneural gene expression subtype, which is associated with enrichment of GSCs. Using multiplexed single-cell qRT-PCR, GSC marker-based cell sorting, database interrogations, and functional assays in GSCs derived from patients' tumors, we establish Grm3 as a novel marker and potential therapeutic target in GSCs. We confirm that Grm3 inhibits adenylyl cyclase and regulates extracellular signal-regulated kinase. Targeting Grm3 disrupts self-renewal and promotes differentiation of GSCs. Thus, we hypothesize that Grm3 signaling may complement oncogenic functions of glutamatergic ionotropic receptor activity in neuroglial synapses, supporting a link between neuronal activity and the GSC phenotype. The novel class of highly specific Grm3 inhibitors that we characterize herein have been clinically tested as cognitive enhancers in humans with a favorable safety profile., (© 2020 The Authors.)

Background: The presentation of pulmonary tuberculosis (PTB) in young children is often clinically indistinguishable from other common respiratory illnesses, which are frequently infections of viral aetiology. As little is known about the role of viruses in children with PTB, we investigated the prevalence of respiratory viruses in children with suspected PTB at presentation and follow-up., Methods: In an observational cohort study, children < 13 years were routinely investigated for suspected PTB in Cape Town, South Africa between December 2015 and September 2017 and followed up for 24 weeks. Nasopharyngeal aspirates (NPAs) were tested for respiratory viruses using multiplex PCR at enrolment, week 4 and 8., Results: Seventy-three children were enrolled [median age 22.0 months; (interquartile range 10.0-48.0); 56.2% male and 17.8% HIV-infected. Anti-tuberculosis treatment was initiated in 54.8%; of these 50.0% had bacteriologically confirmed TB. At enrolment, ≥1 virus were detected in 95.9% (70/73) children; most commonly human rhinovirus (HRV) (74.0%). HRV was more frequently detected in TB cases (85%) compared to ill controls (60.6%) (p = 0.02). Multiple viruses were detected in 71.2% of all children; 80% of TB cases and 60.6% of ill controls (p = 0.07). At follow-up, ≥1 respiratory virus was detected in 92.2% (47/51) at week 4, and 94.2% (49/52) at week 8., Conclusions: We found a high prevalence of viral respiratory co-infections in children investigated for PTB, irrespective of final PTB diagnosis, which remained high during follow up. Future work should include investigating the whole respiratory ecosystem in combination with pathogen- specific immune responses.

Chronic viral infections are often associated with impaired CD8 + T cell function, referred to as exhaustion. Although the molecular and cellular circuits involved in CD8 + T cell exhaustion are well defined, with sustained presence of antigen being one important parameter, how much T cell receptor (TCR) signaling is actually ongoing in vivo during established chronic infection is unclear. Here, we characterize the in vivo TCR signaling of virus-specific exhausted CD8 + T cells in a mouse model, leveraging TCR signaling reporter mice in combination with transcriptomics. In vivo signaling in exhausted cells is low, in contrast to their in vitro signaling potential, and despite antigen being abundantly present. Both checkpoint blockade and adoptive transfer of naïve target cells increase TCR signaling, demonstrating that engagement of co-inhibitory receptors curtails CD8 + T cell signaling and function in vivo.

A hallmark of chronic infections is the presence of exhausted CD8 T cells, characterized by a distinct transcriptional program compared with functional effector or memory cells, co-expression of multiple inhibitory receptors, and impaired effector function, mainly driven by recurrent T cell receptor engagement. In the context of chronic lymphocytic choriomeningitis virus (LCMV) infection in mice, most studies focused on studying splenic virus-specific CD8 T cells. Here, we provide a detailed characterization of exhausted CD8 T cells isolated from six different tissues during established LCMV infection, using single-cell RNA sequencing. Our data reveal that exhausted cells are heterogeneous, adopt organ-specific transcriptomic profiles, and can be divided into five main functional subpopulations: advanced exhaustion, effector-like, intermediate, proliferating, or memory-like. Adoptive transfer experiments showed that these phenotypes are plastic, suggesting that the tissue microenvironment has a major impact in shaping the phenotype and function of virus-specific CD8 T cells during chronic infection., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

In narrow electron bands in which the Coulomb interaction energy becomes comparable to the bandwidth, interactions can drive new quantum phases. Such flat bands in twisted graphene-based systems result in correlated insulator, superconducting and topological states. Here we report evidence of low-energy flat bands in twisted bilayer WSe 2 , with signatures of collective phases observed over twist angles that range from 4 to 5.1°. At half-band filling, a correlated insulator appeared that is tunable with both twist angle and displacement field. At a 5.1° twist, zero-resistance pockets were observed on doping away from half filling at temperatures below 3 K, which indicates a possible transition to a superconducting state. The observation of tunable collective phases in a simple band, which hosts only two holes per unit cell at full filling, establishes twisted bilayer transition metal dichalcogenides as an ideal platform to study correlated physics in two dimensions on a triangular lattice.

Investigations into environmental monitoring (EM) excursions can be prolonged and do not always result in clear root causes or corrective and preventative actions. This article outlines how biofluorescent particle counting (BFPC) can be used in investigations to eliminate the inherent delays of culture-based methods. The application for investigations supplements routine EM, acting as a risk-reduction tool enabling real-time detection of viable microorganisms in air samples and supporting root cause analysis and remedial actions. The article includes guidance on how to use the technology, a real case study involving a mold excursion, and examples of business benefits achieved by various companies., (© PDA, Inc. 2020.)

Experimental advances in the fabrication and characterization of few-layer materials stacked at a relative twist of small angle have recently shown the emergence of flat energy bands. As a consequence electron interactions become relevant, providing inroads into the physics of strongly correlated two-dimensional systems. Here, we demonstrate by combining large scale ab initio simulations with numerically exact strong correlation approaches that an effective one-dimensional system emerges upon stacking two twisted sheets of GeSe, in marked contrast to all moiré systems studied so far. This not only allows to study the necessarily collective nature of excitations in one dimension, but can also serve as a promising platform to scrutinize the crossover from two to one dimension in a controlled setup by varying the twist angle, which provides an intriguing benchmark with respect to theory. We thus establish twisted bilayer GeSe as an intriguing inroad into the strongly correlated physics of lowdimensional systems.

The bilayer Hubbard model with electron-hole doping is an ideal platform to study excitonic orders due to suppressed recombination via spatial separation of electrons and holes. However, suffering from the sign problem, previous quantum Monte Carlo studies could not arrive at an unequivocal conclusion regarding the presence of phases with clear signatures of excitonic condensation in bilayer Hubbard models. Here, we develop a determinant quantum Monte Carlo algorithm for the bilayer Hubbard model that is sign-problem-free for equal and opposite doping in the two layers and study excitonic order and charge and spin density modulations as a function of chemical potential difference between the two layers, on-site Coulomb repulsion, and interlayer interaction. In the intermediate coupling regime and in proximity to the SU(4)-symmetric point, we find a biexcitonic condensate phase at finite electron-hole doping, as well as a competing (π,π) charge density wave state. We extract the Berezinskii-Kosterlitz-Thouless transition temperature from superfluid density and a finite-size scaling analysis of the correlation functions and explain our results in terms of an effective biexcitonic hard-core boson model.

High-density lipoproteins (HDL) contain hundreds of lipid species and proteins and exert many potentially vasoprotective and antidiabetogenic activities on cells. To resolve structure-function-disease relationships of HDL, we characterized HDL of 51 healthy subjects and 98 patients with diabetes (T2DM), coronary heart disease (CHD), or both for protein and lipid composition, as well as functionality in 5 cell types. The integration of 40 clinical characteristics, 34 nuclear magnetic resonance (NMR) features, 182 proteins, 227 lipid species, and 12 functional read-outs by high-dimensional statistical modeling revealed, first, that CHD and T2DM are associated with different changes of HDL in size distribution, protein and lipid composition, and function. Second, different cellular functions of HDL are weakly correlated with each other and determined by different structural components. Cholesterol efflux capacity (CEC) was no proxy of other functions. Third, 3 potentially novel determinants of HDL function were identified and validated by the use of artificially reconstituted HDL, namely the sphingadienine-based sphingomyelin SM 42:3 and glycosylphosphatidylinositol-phospholipase D1 for the ability of HDL to inhibit starvation-induced apoptosis of human aortic endothelial cells and apolipoprotein F for the ability of HDL to promote maximal respiration of brown adipocytes.

Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and require analysis approaches that temporally map between cell state distributions across time points. Current approaches to approximate the single-cell time series as a dynamical system suffer from too restrictive assumptions about the type of kinetics, or link together pairs of sequential measurements in a discontinuous fashion. We propose Dynamic Distribution Decomposition (DDD), an operator approximation approach to infer a continuous distribution map between time points. On the basis of single-cell snapshot time series data, DDD approximates the continuous time Perron-Frobenius operator by means of a finite set of basis functions. This procedure can be interpreted as a continuous time Markov chain over a continuum of states. By only assuming a memoryless Markov (autonomous) process, the types of dynamics represented are more general than those represented by other common models, e.g., chemical reaction networks, stochastic differential equations. Furthermore, we can a posteriori check whether the autonomy assumptions are valid by calculation of prediction error-which we show gives a measure of autonomy within the studied system. The continuity and autonomy assumptions ensure that the same dynamical system maps between all time points, not arbitrarily changing at each time point. We demonstrate the ability of DDD to reconstruct dynamically important cell states and their transitions both on synthetic data, as well as on mass cytometry time series of iPSC reprogramming of a fibroblast system. We use DDD to find previously identified subpopulations of cells and to visualise differentiation trajectories. Dynamic Distribution Decomposition allows interpretation of high-dimensional snapshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynamics analysis for a variety of biological processes by means of identifying their dynamically important cell states., Competing Interests: The authors have declared that no competing interests exist.

Data on integrase resistance patterns in low- and middle-income countries (LMICs) is scarce. We assessed genotypic drug resistance in 43 patients with virological failure on integrase strand transfer inhibitors (INSTIs) containing regimens as part of the third-line treatment program in South Africa. Of the raltegravir (RAL) exposed patients 20/34 (59%) had ≥1 major INSTI mutation, including two (6%) with dolutegravir (DTG) cross-resistance. DTG resistance was detected in one out of four DTG-exposed patients. Replacing RAL with DTG may reduce the risk of INSTI mutations. We recommend DTG drug resistance monitoring when DTG is introduced at a larger scale in LMICs., (© The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte-macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing-remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.

We introduce TreeTop, an algorithm for single cell data analysis to identify and assign a branching score to branch points in biological processes which may have multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T-cell maturation, B-cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallower hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

This paper provides recommendations for quality oversight, manufacturing operations, and industry perspective of regulatory expectations to enable aseptic facilities to move toward real-time and continuous microbiological environmental monitoring, thereby reducing interventions and future replacement of Grade A settle plates and nonremote active air sampling. The replacement of traditional monitoring with biofluorescent particle-counting systems provides an improvement in process understanding and product safety and reduces operator manipulations, assuring product quality and real-time process verification. The future state pharmaceutical technology roadmaps include gloveless isolators with real-time and continuous monitoring for aseptic manufacturing. LAY ABSTRACT: This paper advocates the use of an alternative and relatively new method of monitoring the air for contamination in biopharmaceutical manufacturing facilities. The alternative method is based on a type of instrument the authors refer to as a biofluorescent particle counter (BFPC). The BFPC method has the advantage of being able to detect airborne microorganisms continuously and to record the actual time of detection. The replacement of traditional monitoring with BFPC systems can provide better data, which can be used to improve the understanding of contamination risks in complex manufacturing processes, ultimately providing more confidence in product safety. The authors present data showing the suitability of BFPC. This immediate result is very useful for picking up early any possible contamination and should, therefore, provide a better way to monitor and control the risk of contamination. As traditional monitoring methods require manual manipulation, an additional advantage of BFPC systems is that they can reduce manual manipulations. Elimination of all interventions is a goal in the industry, because although they are tightly controlled, interventions are an unwanted potential source of contamination., (© PDA, Inc. 2019.)

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

The root cap protects the stem cell niche of angiosperm roots from damage. In Arabidopsis, lateral root cap (LRC) cells covering the meristematic zone are regularly lost through programmed cell death, while the outermost layer of the root cap covering the tip is repeatedly sloughed. Efficient coordination with stem cells producing new layers is needed to maintain a constant size of the cap. We present a signalling pair, the peptide IDA-LIKE1 (IDL1) and its receptor HAESA-LIKE2 (HSL2), mediating such communication. Live imaging over several days characterized this process from initial fractures in LRC cell files to full separation of a layer. Enhanced expression of IDL1 in the separating root cap layers resulted in increased frequency of sloughing, balanced with generation of new layers in a HSL2-dependent manner. Transcriptome analyses linked IDL1-HSL2 signalling to the transcription factors BEARSKIN1/2 and genes associated with programmed cell death. Mutations in either IDL1 or HSL2 slowed down cell division, maturation and separation. Thus, IDL1-HSL2 signalling potentiates dynamic regulation of the homeostatic balance between stem cell division and sloughing activity.

Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Gene splicing profiles are frequently altered in cancer, and the splice variants of fibronectin (FN) that contain the extra-domains A (EDA) or B (EDB), referred to as EDA+FN or EDB+FN, are highly upregulated in tumor vasculature. Transforming growth factor β (TGF-β) signaling has been attributed a pivotal role in glioblastoma, with TGF-β promoting angiogenesis and vessel remodeling. By using immunohistochemistry staining, we observed that the oncofetal FN isoforms EDA+FN and EDB+FN are expressed in glioblastoma vasculature. Ex vivo single-cell gene expression profiling of tumors by using CD31 and α-smooth muscle actin (αSMA) as markers for endothelial cells, and pericytes and vascular smooth muscle cells (VSMCs), respectively, confirmed the predominant expression of FN, EDA+FN and EDB+FN in the vascular compartment of glioblastoma. Specifically, within the CD31-positive cell population, we identified a positive correlation between the expression of EDA+FN and EDB+FN, and of molecules associated with TGF-β signaling. Further, TGF-β induced EDA+FN and EDB+FN in human cerebral microvascular endothelial cells and glioblastoma-derived endothelial cells in a SMAD3- and SMAD4-dependent manner. In turn, we found that FN modulated TGF-β superfamily signaling in endothelial cells via the EDA and EDB, pointing towards a bidirectional influence of oncofetal FN and TGF-β superfamily signaling., Competing Interests: Competing interestsM.W. has received research grants from Acceleron, Actelion, Alpinia Institute, Bayer, Isarna, Merk, Sharp & Dohme (MSD), Merck (EMD), Novocure, Piqur Therapeutics, and Roche and honoraria for lectures or advisory board participation or consulting from Celldex, Immunocellular Therapeutics, MSD, Merck (EMD), Novocure, Pfizer, Roche, and Teva. L.Z. is CEO of Sirius-biotech a biotechnology start-up; C.C. is a scientist of Sirius-biotech. The other authors have no financial conflicts of interest., (© 2018. Published by The Company of Biologists Ltd.)

Orthohantaviruses are a group of rodentborne viruses with a worldwide distribution. The orthohantavirus Seoul virus (SEOV) can cause hemorrhagic fever with renal syndrome in humans and is distributed worldwide, like its reservoir host, the rat. Cases of SEOV in wild and pet rats have been described in several countries, and human cases have been reported in the United Kingdom, France, Canada, and the United States. In the Netherlands, SEOV has previously been found in wild brown rats. We describe an autochthonous human case of SEOV infection in the Netherlands. This patient had nonspecific clinical symptoms of an orthohantavirus infection (gastrointestinal symptoms and distinct elevation of liver enzymes). Subsequent source investigation revealed 2 potential sources, the patient's feeder rats and a feeder rat farm. At both sources, a high prevalence of SEOV was found in the rats. The virus closely resembled the Cherwell and Turckheim SEOV strains that were previously found in Europe.

Background: The WHO-recommended first-line antiretroviral therapy (ART) as a fixed dose combination (FDC) of efavirenz (EFV) and tenofovir disoproxil fumarate (TDF) with lamivudine (3TC) or emtricitabine (FTC) has been preferred in the large scale unprecedented ART roll out in Southern Africa. Models and recent reports suggest that pre-ART HIV drug resistance (PDR) is increasing with high treatment coverage., Method: We therefore investigated PDR and any local transmission clusters in a setting where high treatment coverage was further enhanced by universal test and treat (UTT). Surveillance drug resistance mutations (SDRMs) were identified with an in-house PCR and population sequencing method and calibrated population resistance (CPR) tool., Results: Of 60 patients, six (10%) had an SDRM mutation: five (8.3%) had nonnucleoside reverse transcriptase (NNRT) mutations, one had an nucleos(t)ide reverse transcriptase inhibitor mutation and none had protease inhibitor (PI) mutations. Phylogenetic analysis revealed no large transmission clusters., Conclusion: An increase to the current moderate PDR levels and the better tolerability and durability, may support a recent drive to avail FDC integrase strand transfer inhibitor (ISTI)-based regimens as the new preferred first-line ART in the Southern African region for individual benefit and to contribute to limiting transmission of infection and drug resistant virus.

The search for quantum spin liquids in frustrated quantum magnets recently has enjoyed a surge of interest, with various candidate materials under intense scrutiny. However, an experimental confirmation of a gapped topological spin liquid remains an open question. Here, we show that circularly polarized light can provide a knob to drive frustrated Mott insulators into a chiral spin liquid, realizing an elusive quantum spin liquid with topological order. We find that the dynamics of a driven Kagome Mott insulator is well-captured by an effective Floquet spin model, with heating strongly suppressed, inducing a scalar spin chirality S i · (S j  × S k ) term which dynamically breaks time-reversal while preserving SU(2) spin symmetry. We fingerprint the transient phase diagram and find a stable photo-induced chiral spin liquid near the equilibrium state. The results presented suggest employing dynamical symmetry breaking to engineer quantum spin liquids and access elusive phase transitions that are not readily accessible in equilibrium.

In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5'-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%.

Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to Escherichia coli , Saccharomyces cerevisiae , Thermus thermophilus , and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species., (Copyright © 2017, American Association for the Advancement of Science.)

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso., Competing Interests: The authors have declared that no competing interests exist.

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Monolayer transition-metal dichalcogenides are novel materials which at low energies constitute a condensed-matter realization of massive relativistic fermions in two dimensions. Here, we show that this picture breaks for optical pumping-instead, the added complexity of a realistic materials description leads to a new mechanism to optically induce topologically protected chiral edge modes, facilitating optically switchable conduction channels that are insensitive to disorder. In contrast to graphene and previously discussed toy models, the underlying mechanism relies on the intrinsic three-band nature of transition-metal dichalcogenide monolayers near the band edges. Photo-induced band inversions scale linearly in applied pump field and exhibit transitions from one to two chiral edge modes on sweeping from red to blue detuning. We develop an ab initio strategy to understand non-equilibrium Floquet-Bloch bands and topological transitions, and illustrate for WS 2 that control of chiral edge modes can be dictated solely from symmetry principles and is not qualitatively sensitive to microscopic materials details.