Mechanical Engineering - Research Publications
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ItemImplementation and Evaluation of an Embedded LES-RANS Solver(SPRINGER, 2017-04)
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ItemThe Effect of Wall Normal Actuation on a Turbulent Boundary Layer(SPRINGER, 2017-12)
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ItemNo Preview AvailableThe human cap-binding complex is functionally connected to the nuclear RNA exosome.(Springer Science and Business Media LLC, 2013-12)Nuclear processing and quality control of eukaryotic RNA is mediated by the RNA exosome, which is regulated by accessory factors. However, the mechanism of exosome recruitment to its ribonucleoprotein (RNP) targets remains poorly understood. Here we report a physical link between the human exosome and the cap-binding complex (CBC). The CBC associates with the ARS2 protein to form CBC-ARS2 (CBCA) and then further connects, together with the ZC3H18 protein, to the nuclear exosome targeting (NEXT) complex, thus forming CBC-NEXT (CBCN). RNA immunoprecipitation using CBCN factors as well as the analysis of combinatorial depletion of CBCN and exosome components underscore the functional relevance of CBC-exosome bridging at the level of target RNA. Specifically, CBCA suppresses read-through products of several RNA families by promoting their transcriptional termination. We suggest that the RNP 5' cap links transcription termination to exosomal RNA degradation through CBCN.
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ItemLaser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling.(Springer Science and Business Media LLC, 2016-07-08)Laser capture microscopy (LCM) coupled with global transcriptome profiling could enable precise analyses of cell populations without the need for tissue dissociation, but has so far required relatively large numbers of cells. Here we report a robust and highly efficient strategy for LCM coupled with full-length mRNA-sequencing (LCM-seq) developed for single-cell transcriptomics. Fixed cells are subjected to direct lysis without RNA extraction, which both simplifies the experimental procedures as well as lowers technical noise. We apply LCM-seq on neurons isolated from mouse tissues, human post-mortem tissues, and illustrate its utility down to single captured cells. Importantly, we demonstrate that LCM-seq can provide biological insight on highly similar neuronal populations, including motor neurons isolated from different levels of the mouse spinal cord, as well as human midbrain dopamine neurons of the substantia nigra compacta and the ventral tegmental area.
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Itemscphaser: haplotype inference using single-cell RNA-seq data.(Oxford University Press (OUP), 2016-10-01)UNLABELLED: Determination of haplotypes is important for modelling the phenotypic consequences of genetic variation in diploid organisms, including cis-regulatory control and compound heterozygosity. We realized that single-cell RNA-seq (scRNA-seq) data are well suited for phasing genetic variants, since both transcriptional bursts and technical bottlenecks cause pronounced allelic fluctuations in individual single cells. Here we present scphaser, an R package that phases alleles at heterozygous variants to reconstruct haplotypes within transcribed regions of the genome using scRNA-seq data. The devised method efficiently and accurately reconstructed the known haplotype for ≥93% of phasable genes in both human and mouse. It also enables phasing of rare and de novo variants and variants far apart within genes, which is hard to attain with population-based computational inference. AVAILABILITY AND IMPLEMENTATION: scphaser is implemented as an R package. Tutorial and code are available at https://github.com/edsgard/scphaser CONTACT: rickard.sandberg@ki.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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ItemPosition- and Hippo signaling-dependent plasticity during lineage segregation in the early mouse embryo.(eLife Sciences Publications, Ltd, 2017-02-22)The segregation of the trophectoderm (TE) from the inner cell mass (ICM) in the mouse blastocyst is determined by position-dependent Hippo signaling. However, the window of responsiveness to Hippo signaling, the exact timing of lineage commitment and the overall relationship between cell commitment and global gene expression changes are still unclear. Single-cell RNA sequencing during lineage segregation revealed that the TE transcriptional profile stabilizes earlier than the ICM and prior to blastocyst formation. Using quantitative Cdx2-eGFP expression as a readout of Hippo signaling activity, we assessed the experimental potential of individual blastomeres based on their level of Cdx2-eGFP expression and correlated potential with gene expression dynamics. We find that TE specification and commitment coincide and occur at the time of transcriptional stabilization, whereas ICM cells still retain the ability to regenerate TE up to the early blastocyst stage. Plasticity of both lineages is coincident with their window of sensitivity to Hippo signaling.
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ItemQ&A: using Patch-seq to profile single cells.(Springer Science and Business Media LLC, 2017-07-06)Individual neurons vary widely in terms of their gene expression, morphology, and electrophysiological properties. While many techniques exist to study single-cell variability along one or two of these dimensions, very few techniques can assess all three features for a single cell. We recently developed Patch-seq, which combines whole-cell patch clamp recording with single-cell RNA-sequencing and immunohistochemistry to comprehensively profile the transcriptomic, morphologic, and physiologic features of individual neurons. Patch-seq can be broadly applied to characterize cell types in complex tissues such as the nervous system, and to study the transcriptional signatures underlying the multidimensional phenotypes of single cells.
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ItemMouse Model of Alagille Syndrome and Mechanisms of Jagged1 Missense Mutations.(Elsevier BV, 2018-03)BACKGROUND & AIMS: Alagille syndrome is a genetic disorder characterized by cholestasis, ocular abnormalities, characteristic facial features, heart defects, and vertebral malformations. Most cases are associated with mutations in JAGGED1 (JAG1), which encodes a Notch ligand, although it is not clear how these contribute to disease development. We aimed to develop a mouse model of Alagille syndrome to elucidate these mechanisms. METHODS: Mice with a missense mutation (H268Q) in Jag1 (Jag1+/Ndr mice) were outbred to a C3H/C57bl6 background to generate a mouse model for Alagille syndrome (Jag1Ndr/Ndr mice). Liver tissues were collected at different timepoints during development, analyzed by histology, and liver organoids were cultured and analyzed. We performed transcriptome analysis of Jag1Ndr/Ndr livers and livers from patients with Alagille syndrome, cross-referenced to the Human Protein Atlas, to identify commonly dysregulated pathways and biliary markers. We used species-specific transcriptome separation and ligand-receptor interaction assays to measure Notch signaling and the ability of JAG1Ndr to bind or activate Notch receptors. We studied signaling of JAG1 and JAG1Ndr via NOTCH 1, NOTCH2, and NOTCH3 and resulting gene expression patterns in parental and NOTCH1-expressing C2C12 cell lines. RESULTS: Jag1Ndr/Ndr mice had many features of Alagille syndrome, including eye, heart, and liver defects. Bile duct differentiation, morphogenesis, and function were dysregulated in newborn Jag1Ndr/Ndr mice, with aberrations in cholangiocyte polarity, but these defects improved in adult mice. Jag1Ndr/Ndr liver organoids collapsed in culture, indicating structural instability. Whole-transcriptome sequence analyses of liver tissues from mice and patients with Alagille syndrome identified dysregulated genes encoding proteins enriched at the apical side of cholangiocytes, including CFTR and SLC5A1, as well as reduced expression of IGF1. Exposure of Notch-expressing cells to JAG1Ndr, compared with JAG1, led to hypomorphic Notch signaling, based on transcriptome analysis. JAG1-expressing cells, but not JAG1Ndr-expressing cells, bound soluble Notch1 extracellular domain, quantified by flow cytometry. However, JAG1 and JAG1Ndr cells each bound NOTCH2, and signaling from NOTCH2 signaling was reduced but not completely inhibited, in response to JAG1Ndr compared with JAG1. CONCLUSIONS: In mice, expression of a missense mutant of Jag1 (Jag1Ndr) disrupts bile duct development and recapitulates Alagille syndrome phenotypes in heart, eye, and craniofacial dysmorphology. JAG1Ndr does not bind NOTCH1, but binds NOTCH2, and elicits hypomorphic signaling. This mouse model can be used to study other features of Alagille syndrome and organ development.
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ItemLarge-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition(American Society of Mechanical Engineers, 2019-12-01)In low-pressure turbines (LPTs), around 60–70% of losses are generated away from end-walls, while the remaining 30–40% is controlled by the interaction of the blade profile with the end-wall boundary layer. Experimental and numerical studies have shown how the strength and penetration of the secondary flow depends on the characteristics of the incoming end-wall boundary layer. Experimental techniques did shed light on the mechanism that controls the growth of the secondary vortices, and scale-resolving computational fluid dynamics (CFD) allowed to dive deep into the details of the vorticity generation. Along these lines, this paper discusses the end-wall flow characteristics of the T106 LPT profile at Re = 120 K and M = 0.59 by benchmarking with experiments and investigating the impact of the incoming boundary layer state. The simulations are carried out with proven Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) solvers to determine if Reynolds-averaged models can capture the relevant flow details with enough accuracy to drive the design of this flow region. Part I of the paper focuses on the critical grid needs to ensure accurate LES and on the analysis of the overall time-averaged flow field and comparison between RANS, LES, and measurements when available. In particular, the growth of secondary flow features, the trace and strength of the secondary vortex system, and its impact on the blade load variation along the span and end-wall flow visualizations are analyzed. The ability of LES and RANS to accurately predict the secondary flows is discussed together with the implications this has on design.
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ItemApplying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot(American Society of Mechanical Engineers, 2018-10-01)Machine learning was applied to large-eddy simulation (LES) data to develop nonlinear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A gene expression programming (GEP) based algorithm was used to symbolically regress novel nonlinear explicit algebraic stress models and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Steady Reynolds-averaged Navier–Stokes (RANS) calculations were then conducted with the new explicit algebraic stress models. The best overall agreement with LES data was found when selecting the near wall region, where high levels of anisotropy exist, as training region, and using the mean squared error of the anisotropy tensor as cost function. For the thin lip geometry, the adiabatic wall effectiveness was predicted in good agreement with the LES and experimental data when combining the GEP-trained model with the standard eddy-diffusivity model. Crucially, the same model combination also produced significant improvement in the predictive accuracy of adiabatic wall effectiveness for different blowing ratios (BRs), despite not having seen those in the training process. For the thick lip case, the match with reference values deteriorated due to the presence of large-scale, relative to slot height, vortex shedding. A GEP-trained scalar flux model, in conjunction with a trained RANS model, was found to significantly improve the prediction of the adiabatic wall effectiveness.