Pre-activated antiviral innate immunity in the upper airways controls early SARS-CoV-2 infection in children.

Children have reduced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates and a substantially lower risk for developing severe coronavirus disease 2019 compared with adults. However, the molecular mechanisms underlying protection in younger age groups remain unknown. Here we characterize the single-cell transcriptional landscape in the upper airways of SARS-CoV-2-negative (n = 18) and age-matched SARS-CoV-2-positive (n = 24) children and corresponding samples from adults (n = 44), covering an age range of 4 weeks to 77 years. Children displayed higher basal expression of relevant pattern recognition receptors such as MDA5 (IFIH1) and RIG-I (DDX58) in upper airway epithelial cells, macrophages and dendritic cells, resulting in stronger innate antiviral responses upon SARS-CoV-2 infection than in adults. We further detected distinct immune cell subpopulations including KLRC1 (NKG2A)+ cytotoxic T cells and a CD8+ T cell population with a memory phenotype occurring predominantly in children. Our study provides evidence that the airway immune cells of children are primed for virus sensing, resulting in a stronger early innate antiviral response to SARS-CoV-2 infection than in adults.

TGFß-induced cytoskeletal remodeling mediates elevation of cell stiffness and invasiveness in NSCLC.

Importance of growth factor (GF) signaling in cancer progression is widely acknowledged. Transforming growth factor beta (TGFß) is known to play a key role in epithelial-to-mesenchymal transition (EMT) and metastatic cell transformation that are characterized by alterations in cell mechanical architecture and behavior towards a more robust and motile single cell phenotype. However, mechanisms mediating cancer type specific enhancement of cell mechanical phenotype in response to TGFß remain poorly understood. Here, we combine high-throughput mechanical cell phenotyping, microarray analysis and gene-silencing to dissect cytoskeletal mediators of TGFß-induced changes in mechanical properties of on-small-cell lung carcinoma (NSCLC) cells. Our experimental results show that elevation of rigidity and invasiveness of TGFß-stimulated NSCLC cells correlates with upregulation of several cytoskeletal and motor proteins including vimentin, a canonical marker of EMT, and less-known unconventional myosins. Selective probing of gene-silenced cells lead to identification of unconventional myosin MYH15 as a novel mediator of elevated cell rigidity and invasiveness in TGFß-stimulated NSCLC cells. Our experimental results provide insights into TGFß-induced cytoskeletal remodeling of NSCLC cells and suggest that mediators of elevated cell stiffness and migratory activity such as unconventional cytoskeletal and motor proteins may represent promising pharmaceutical targets for restraining invasive spread of lung cancer.

Quantification of substrate and cellular strains in stretchable 3D cell cultures: an experimental and computational framework.

The mechanical cell environment is a key regulator of biological processes . In living tissues, cells are embedded into the 3D extracellular matrix and permanently exposed to mechanical forces. Quantification of the cellular strain state in a 3D matrix is therefore the first step towards understanding how physical cues determine single cell and multicellular behaviour. The majority of cell assays are, however, based on 2D cell cultures that lack many essential features of the in vivo cellular environment. Furthermore, nondestructive measurement of substrate and cellular mechanics requires appropriate computational tools for microscopic image analysis and interpretation. Here, we present an experimental and computational framework for generation and quantification of the cellular strain state in 3D cell cultures using a combination of 3D substrate stretcher, multichannel microscopic imaging and computational image analysis. The 3D substrate stretcher enables deformation of living cells embedded in bead-labelled 3D collagen hydrogels. Local substrate and cell deformations are determined by tracking displacement of fluorescent beads with subsequent finite element interpolation of cell strains over a tetrahedral tessellation. In this feasibility study, we debate diverse aspects of deformable 3D culture construction, quantification and evaluation, and present an example of its application for quantitative analysis of a cellular model system based on primary mouse hepatocytes undergoing transforming growth factor (TGF-ß) induced epithelial-to-mesenchymal transition.

Mutant KIT as imatinib-sensitive target in metastatic sinonasal carcinoma.

Background: Sinonasal carcinomas (SNCs) comprise various rare tumor types that are characterized by marked histologic diversity and largely unknown molecular profiles, yet share an overall poor prognosis owing to an aggressive clinical course and frequent late-stage diagnosis. The lack of effective systemic therapies for locally advanced or metastatic SNC poses a major challenge to therapeutic decision making for individual patients. We here aimed to identify actionable genetic alterations in a patient with metastatic SNC whose tumor, despite all diagnostic efforts, could not be assigned to any known SNC category and was refractory to multimodal therapy. Patients and methods: We used whole-exome and transcriptome sequencing to identify a KIT exon 11 mutation (c.1733_1735del, p.D579del) as potentially druggable target in this patient and carried out cancer hotspot panel sequencing to detect secondary resistance-conferring mutations in KIT. Furthermore, as a step towards clinical exploitation of the recently described signatures of mutational processes in cancer genomes, we established and applied a novel bioinformatics algorithm that enables supervised analysis of the mutational catalogs of individual tumors. Results: Molecularly guided treatment with imatinib in analogy to the management of gastrointestinal stromal tumor (GIST) resulted in a dramatic and durable response with remission of nearly all tumor manifestations, indicating a dominant driver function of mutant KIT in this tumor. KIT dependency was further validated by a secondary KIT exon 17 mutation (c.2459_2462delATTCinsG, p.D820_S821delinsG) that was detected upon tumor progression after 10 months of imatinib treatment and provided a rationale for salvage therapy with regorafenib, which has activity against KIT exon 11/17 mutant GIST. Conclusions: These observations highlight the potential of unbiased genomic profiling for uncovering the vulnerabilities of individual malignancies, particularly in rare and unclassifiable tumors, and underscore that KIT exon 11 mutations represent tractable therapeutic targets across different histologies.

Death receptor-based enrichment of Cas9-expressing cells.

BACKGROUND: The CRISPR/Cas9 genome editing system has greatly facilitated and expanded our capacity to engineer mammalian genomes, including targeted gene knock-outs. However, the phenotyping of the knock-out effect requires a high DNA editing efficiency. RESULTS: Here, we report a user-friendly strategy based on the extrinsic apoptosis pathway that allows enrichment of a polyclonal gene-edited cell population, by selecting Cas9-transfected cells that co-express dominant-negative mutants of death receptors. The extrinsic apoptosis pathway can be triggered in many mammalian cell types, and ligands are easy to produce, do not require purification and kill much faster than the state-of-the-art selection drug puromycin. Stringent assessment of our advanced selection strategy via Sanger sequencing, T7 endonuclease I (T7E1) assay and direct phenotyping confirmed a strong and rapid enrichment of Cas9-expressing cell populations, in some cases reaching up to 100 % within one hour. Notably, the efficiency of target DNA cleavage in these enriched cells reached high levels that exceeded the reliable range of the T7E1 assay, a conclusion that can be generalized for editing efficiencies above 30 %. Moreover, our data emphasize that the insertion and deletion pattern induced by a specific gRNA is reproducible across different cell lines. CONCLUSIONS: The workflow and the findings reported here should streamline a wide array of future low- or high-throughput gene knock-out screens, and should largely improve data interpretation from CRISPR experiments.

Loss of function of PGAP1 as a cause of severe encephalopathy identified by Whole Exome Sequencing: Lessons of the bioinformatics pipeline.

We evaluated a multiple consanguineous Turkish family with two children, a boy and a girl, affected by severe encephalopathy, hypotonia, microcephaly and retinal dystrophy by a combination of linkage analysis and Whole Exome Sequencing (WES). We analyzed the sequence data by two different bioinformatics pipelines which did not differ in overall processing strategy but involved differences in software used, minor allele frequency (MAF) thresholds and reference data sets, the usage of in-house control exomes and filter settings to prioritize called variants. Assuming autosomal recessive mode of inheritance, only homozygous variants present in both children were considered. The resulting variant lists differed partially (nine variants identified by both pipelines, ten variants by only one pipeline). Major reasons for this discrepancy were different filters for MAF and different variant prioritizations. Combining the variant lists with the results of linkage analysis and further prioritization by expression data and prediction tools, an intronic homozygous splice variant (c.1090-2A>G; IVS9-2A>G; p.?) in PGAP1 (Post-GPI Attachment To Proteins 1) was identified and validated by cDNA analysis. PGAP1 ensures the first step of maturation of GPI (glycosylphosphatidylinositol)-anchor proteins. Recently, a homozygous loss-of-function mutation in PGAP1 has been reported in one family with two children affected by a similar phenotype. The present report not only illustrates the possible influence of specific filtering settings on the results of WES but also confirms PGAP1 as a cause of severe encephalopathy.

The mutational pattern of primary lymphoma of the central nervous system determined by whole-exome sequencing.

To decipher the mutational pattern of primary CNS lymphoma (PCNSL), we performed whole-exome sequencing to a median coverage of 103 × followed by mutation verification in 9 PCNSL and validation using Sanger sequencing in 22 PCNSL. We identified a median of 202 (range: 139-251) potentially somatic single nucleotide variants (SNV) and 14 small indels (range: 7-22) with potentially protein-changing features per PCNSL. Mutations affected the B-cell receptor, toll-like receptor, and NF-κB and genes involved in chromatin structure and modifications, cell-cycle regulation, and immune recognition. A median of 22.2% (range: 20.0-24.7%) of somatic SNVs in 9 PCNSL overlaps with the RGYW motif targeted by somatic hypermutation (SHM); a median of 7.9% (range: 6.2-12.6%) affects its hotspot position suggesting a major impact of SHM on PCNSL pathogenesis. In addition to the well-known targets of aberrant SHM (aSHM) (PIM1), our data suggest new targets of aSHM (KLHL14, OSBPL10, and SUSD2). Among the four most frequently mutated genes was ODZ4 showing protein-changing mutations in 4/9 PCNSL. Together with mutations affecting CSMD2, CSMD3, and PTPRD, these findings may suggest that alterations in genes having a role in CNS development may facilitate diffuse large B-cell lymphoma manifestation in the CNS. This may point to intriguing mechanisms of CNS tropism in PCNSL.

Caspase-8 cleaves its substrates from the plasma membrane upon CD95-induced apoptosis.

Apoptosis occurs through a tightly regulated cascade of caspase activation. In the context of extrinsic apoptosis, caspase-8 is activated by dimerization inside a death receptor complex, cleaved by auto-proteolysis and subsequently released into the cytosol. This fully processed form of caspase-8 is thought to cleave its substrates BID and caspase-3. To test if the release is required for substrate cleavage, we developed a novel approach based on localization probes to quantitatively characterize the spatial-temporal activity of caspases in living single cells. Our study reveals that caspase-8 is significantly more active at the plasma membrane than within the cytosol upon CD95 activation. This differential activity is controlled by the cleavage of caspase-8 prodomain. As a consequence, targeting of caspase-8 substrates to the plasma membrane can significantly accelerate cell death. Subcellular compartmentalization of caspase-8 activity may serve to restrict enzymatic activity before mitochondrial pathway activation and offers new possibilities to interfere with apoptotic sensitivity of the cells.

Caspase-8 activity has an essential role in CD95/Fas-mediated MAPK activation.

Stimulation of CD95/Fas/APO-1 results in the induction of both apoptotic and non-apoptotic signaling pathways. The processes regulating these two opposing pathways have not been thoroughly elucidated to date. In this study, using quantitative immunoblots, imaging, and mathematical modeling, we addressed the dynamics of the DED proteins of the death-inducing signaling complex (DISC), procaspase-8, and cellular FLICE inhibitory proteins (c-FLIPs) to the onset of CD95-mediated ERK1/2 and p38 mitogen-activated protein kinase (MAPK) activation. We found that CD95 DISC-induced caspase-8 activity is important for the initiation of ERK1/2 and p38 MAPK activation. The long c-FLIP isoform, c-FLIP(L), and the short c-FLIP isoform, c-FLIP(R), inhibited MAPK induction by blocking caspase-8 processing at the DISC. Furthermore, we built a mathematical model describing CD95 DISC-mediated MAPK activation and apoptosis. The model quantitatively defined the dynamics of DED proteins, procaspase-8, and c-FLIP, which lead to caspase-8 activation and induction of apoptotic and non-apoptotic signaling pathways. In conclusion, the combination of biochemical analysis with mathematical modeling provides evidence for an important role of caspase-8 in CD95-mediated activation of MAPKs, while c-FLIP exerts a regulatory function in this process.

Contactless determination of nuclear compressibility using 3D image- and model-based analysis of drug-induced cellular deformation.

Mechanical properties of the chromatin-bearing nucleus in normal and pathological cells are of general interest for epigenetics and medicine. Conventional techniques for quantitative measurements of material properties of cellular matter are based on application of controlled forces onto the cellular or nuclear boundary and do not allow probing intracellular structures that are not directly accessible for physical contact inside the living cell. In this work, we present a novel approach for contactless determination of the nuclear compressibility (i.e. the Poisson's ratio ν) in living cells by means of image- and model-based analysis of drug-induced cell deformation. The Poisson's ratio of the HeLa cell nucleus is determined from time-series of 3D images as a parameter of constitutive model that minimizes the dissimilarity between the numerically predicted and experimentally observed images.

Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients.

Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict four clinical endpoints, and classification performances were compared in terms of accuracy, area under the curve, Matthews correlation coefficient and root mean-squared error. Whereas prediction performance varied with distinct clinical endpoints and classification models, equivalent performance metrics were observed for one- and two-color measurements in both internal and external validation. Furthermore, overlap of selected signature genes correlated inversely with endpoint prediction difficulty. In summary, our data strongly substantiate that the choice of platform is not a primary factor for successful gene expression based-prediction of clinical endpoints.

Integrated genomic profiling identifies two distinct molecular subtypes with divergent outcome in neuroblastoma with loss of chromosome 11q.

Imbalances in chromosome 11q occur in approximately 30% of primary neuroblastoma and are associated with poor outcome. It has been suggested that 11q loss constitutes a distinct clinico-genetic neuroblastoma subgroup by affecting expression levels of corresponding genes. This study analysed the relationship of 11q loss, clinical phenotype and global transcriptomic profiles in four clinico-genetic subgroups (11q alteration/favourable outcome, n=7; 11q alteration/unfavourable outcome, n=14; no 11q alteration/favourable outcome, n=81; no 11q alteration/unfavourable outcome, n=8; tumours with MYCN amplification and/or 1p loss were excluded). Unsupervised and supervised comparisons of gene expression profiles consistently showed significantly different mRNA patterns between favourable and unfavourable neuroblastomas, both in the subgroups with and without 11q loss. In contrast, favourable tumours with and without 11q loss showed highly similar transcriptomic profiles. Disproportionate downregulation of 11q genes was observed only in unfavourable tumours with 11q loss. The diverging molecular profiles were neither caused by considerable differences in the size of the deleted regions nor by differential methylation patterns of 11q genes. Together, this study shows that neuroblastoma with 11q loss comprises two biological subgroups that differ both in their clinical phenotype and gene expression patterns, indicating that 11q loss is not a primary determinant of neuroblastoma tumour behaviour.

Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences.

Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images. The deterministic approaches follow a traditional two-step paradigm comprising particle localization based on either the spot-enhancing filter or 2D Gaussian fitting, as well as motion correspondence based on a global nearest neighbor scheme. Our probabilistic approaches are based on particle filters. We describe approaches based on a mixture of particle filters and based on independent particle filters. For the latter, we have developed a penalization strategy that prevents the problem of filter coalescence (merging) in cases where objects lie in close proximity. A quantitative comparison based on synthetic image sequences is carried out to evaluate the performance of our approaches. In total, eight different tracking approaches have been evaluated. We have also applied these approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approaches based on independent particle filters are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.

Shape normalization of 3D cell nuclei using elastic spherical mapping.

Topological analysis of cells and subcellular structures on the basis of image data, is one of the major trends in modern quantitative biology. However, due to the dynamic nature of cell biology, the optical appearance of different cells or even time-series of the same cell is undergoing substantial variations in shape and texture, which makes a comparison of shapes and distances across different cells a nontrivial task. In the absence of canonical invariances, a natural approach to the normalization of cells consists of spherical mapping, enabling the analysis of targeted regions in terms of canonical spherical coordinates, that is, radial distances and angles. In this work, we present a physically-based approach to spherical mapping, which has been applied for topological analysis of multichannel confocal laser scanning microscopy images of human fibroblast nuclei. Our experimental results demonstrate that spherical mapping of entire nuclear domains can automatically be obtained by inverting affine and elastic transformations, performed on a spherical finite element template mesh.

Nonrigid registration of 3-d multichannel microscopy images of cell nuclei.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.

Automated classification of mitotic phenotypes of human cells using fluorescent proteins.

High-throughput screens of the gene function provide rapidly increasing amounts of data. In particular, the analysis of image data acquired in genome-wide cell phenotype screens constitutes a substantial bottleneck in the evaluation process and motivates the development of automated image analysis tools for large-scale experiments. In this chapter, we present a computational scheme to process multicell time-lapse images as they are produced in high-throughput screens. We describe an approach to automatically segment and classify cell nuclei into different mitotic phenotypes. This enables automated identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves high classification accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.

3D finite element analysis of uniaxial cell stretching: from image to insight.

Mechanical forces play an important role in many microbiological phenomena such as embryogenesis, regeneration, cell proliferation and differentiation. Micromanipulation of cells in a controlled environment is a widely used approach for understanding cellular responses with respect to external mechanical forces. While modern micromanipulation and imaging techniques provide useful optical information about the change of overall cell contours under the impact of external loads, the intrinsic mechanisms of energy and signal propagation throughout the cell structure are usually not accessible by direct observation. This work deals with the computational modelling and simulation of intracellular strain state of uniaxially stretched cells captured in a series of images. A nonlinear elastic finite element method on tetrahedral grids was applied for numerical analysis of inhomogeneous stretching of a rat embryonic fibroblast 52 (REF 52) using a simplified two-component model of a eukaryotic cell consisting of a stiffer nucleus surrounded by a softer cytoplasm. The difference between simulated and experimentally observed cell contours is used as a feedback criterion for iterative estimation of canonical material parameters of the two-component model such as stiffness and compressibility. Analysis of comparative simulations with varying material parameters shows that (i) the ratio between the stiffness of cell nucleus and cytoplasm determines intracellular strain distribution and (ii) large deformations result in increased stiffness and decreased compressibility of the cell cytoplasm. The proposed model is able to reproduce the evolution of the cellular shape over a sequence of observed deformations and provides complementary information for a better understanding of mechanical cell response.

Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis.

Mathematical modeling is required for understanding the complex behavior of large signal transduction networks. Previous attempts to model signal transduction pathways were often limited to small systems or based on qualitative data only. Here, we developed a mathematical modeling framework for understanding the complex signaling behavior of CD95(APO-1/Fas)-mediated apoptosis. Defects in the regulation of apoptosis result in serious diseases such as cancer, autoimmunity, and neurodegeneration. During the last decade many of the molecular mechanisms of apoptosis signaling have been examined and elucidated. A systemic understanding of apoptosis is, however, still missing. To address the complexity of apoptotic signaling we subdivided this system into subsystems of different information qualities. A new approach for sensitivity analysis within the mathematical model was key for the identification of critical system parameters and two essential system properties: modularity and robustness. Our model describes the regulation of apoptosis on a systems level and resolves the important question of a threshold mechanism for the regulation of apoptosis.

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care.

In this paper, we review the results of BIOINFOMED, a study funded by the European Commission (EC) with the purpose to analyse the different issues and challenges in the area where Medical Informatics and Bioinformatics meet. Traditionally, Medical Informatics has been focused on the intersection between computer science and clinical medicine, whereas Bioinformatics have been predominantly centered on the intersection between computer science and biological research. Although researchers from both areas have occasionally collaborated, their training, objectives and interests have been quite different. The results of the Human Genome and related projects have attracted the interest of many professionals, and introduced new challenges that will transform biomedical research and health care. A characteristic of the 'post genomic' era will be to correlate essential genotypic information with expressed phenotypic information. In this context, Biomedical Informatics (BMI) has emerged to describe the technology that brings both disciplines (BI and MI) together to support genomic medicine. In recognition of the dynamic nature of BMI, institutions such as the EC have launched several initiatives in support of a research agenda, including the BIOINFOMED study.