Recensie van A.J. Rinzema† en L. Van Beek, Sicke Benninge en zijn kroniek. Een Groninger burger over opkomst en verval van zijn stad rond 1500

<p>A.J. Rinzema†, <em>Sicke Benninge en zijn kroniek. Een Groninger burger over opkomst en verval van zijn stad rond 1500</em><em>, </em>L. van Beek, met medewerking van D.E.H. de Boer en C.W. Zwart (eds.) (Den Haag: Huygens ING, 2014, 340 pp., isbn 978 90 5216 000 9).</p

A computational pipeline for the diagnosis of CVID patients

Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub) classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (+/- 0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (+/- 0.23) is achieved

GroupEnc: encoder with group loss for global structure preservation

Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion of structure preservation at both local and global levels to create a deep learning model, based on a variational autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm. Our encoder model, called GroupEnc, uses a 'group loss' function to create embeddings with less global structure distortion than VAEs do, while keeping the model parametric and the architecture flexible. We validate our approach using publicly available biological single-cell transcriptomic datasets, employing RNX curves for evaluation.Comment: Submitted to BNAIC/BeNeLearn 202

CytoNorm : a normalization algorithm for cytometry data

High-dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high-content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross-sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single-cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population-specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset-specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch-specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real-world clinical data sets. Overall, our method compared favorably to standard normalization procedures

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient)

Presence of innate lymphoid cells in allogeneic hematopoietic grafts correlates with reduced graft-versus-host disease.

BACKGROUND Allogeneic hematopoietic cell transplantation (HCT) can be devastating when graft-versus-host disease (GvHD) develops. GvHD is characterized by mucosal inflammation due to breaching of epithelial barriers. Innate lymphoid cells (ILCs) are immune modulatory cells that are important in the maintenance of epithelial barriers, via their production of interleukin (IL)-22 and their T cell suppressive properties. After chemo- and radiotherapy, ILCs are depleted, and recovery after remission-induction therapy and after allogeneic HCT is slow and incomplete in a significant number of patients, which is associated with an increased risk to develop acute GvHD. OBJECTIVE To investigate whether the presence of mature ILCs within G-CSF-mobilized HCT grafts is correlated with the development of acute GvHD after allogeneic HCT. STUDY DESIGN We analyzed ILCs in a cohort of 36 patients who received allogeneic HCT for a hematologic malignancy, by flow-cytometric immune-phenotyping of prospectively collected, cryopreserved peripheral blood mononuclear cells (PBMCs) and donor-derived HCT grafts collected for the same patients. Biased analysis, with ILCs defined as CD3-lineage-CD45+CD127+CD161+ lymphocytes, was performed using FlowJo version 10 software. Unbiased analysis was done using FlowSOM, which uses a self-organizing map (SOM) with a minimal spanning tree (MST) to define and visualize different clusters present in the samples. RESULTS Remission-induction therapy significantly depleted ILCs from the blood, and patients who had a relatively low percentage of ILCs before allogeneic HCT were significantly more prone to develop acute GvHD, confirming previous findings in a separate cohort. Allogeneic HCT grafts, which were all obtained from the blood of G-CSF-mobilized healthy donors, contained ILCs at a frequency very similar to the peripheral blood of healthy individuals. The ILC subset composition was also comparable to that of the blood of healthy individuals, with the exception of NKp44+ ILC3s, which were significantly more abundant in HCT grafts. The relative ILC content of the graft tended to correlate with ILC reconstitution after allogeneic HCT, suggesting that peripheral expansion of transplanted mature ILCs may contribute to early ILC reconstitution after allogeneic HCT. Patients who received a relatively ILC-poor HCT graft had a significantly increased risk to develop acute GvHD, compared with patients who received relatively ILC-rich allogeneic HCT grafts. Unbiased phenotypic analysis with the FlowSOM algorithm confirmed that allogeneic HCT grafts of patients who developed acute GvHD contained a lower frequency of ILCs that clustered in NKp44+ ILC3 signature groups. CONCLUSION The presence of ILCs in allogeneic HCT grafts is associated with a reduced risk to develop acute GvHD. These data suggest that enhancement of ILC reconstitution of ILC3s in particular, for example via adoptive transfer of ILCs, may prevent acute GvHD and has the potential to improve outcome of allogeneic HCT recipients

Stabilization of cytokine mRNAs in iNKT cells requires the serine-threonine kinase IRE1alpha

Activated invariant natural killer T (iNKT) cells rapidly produce large amounts of cytokines, but how cytokine mRNAs are induced, stabilized and mobilized following iNKT activation is still unclear. Here we show that an endoplasmic reticulum stress sensor, inositol-requiring enzyme 1 alpha (IRE1 alpha), links key cellular processes required for iNKT cell effector functions in specific iNKT subsets, in which TCR-dependent activation of IRE1 alpha is associated with downstream activation of p38 MAPK and the stabilization of preformed cytokine mRNAs. Importantly, genetic deletion of IRE1 alpha in iNKT cells reduces cytokine production and protects mice from oxazolone colitis. We therefore propose that an IRE1 alpha-dependent signaling cascade couples constitutive cytokine mRNA expression to the rapid induction of cytokine secretion and effector functions in activated iNKT cells

The ORMDL3 asthma susceptibility gene regulates systemic ceramide levels without altering key asthma features in mice

Background: Genome-wide association studies in asthma have repeatedly identified single nucleotide polymorphisms in the ORM (yeast)-like protein isoform 3 (ORMDL3) gene across different populations. Although the ORM homologues in yeast are well-known inhibitors of sphingolipid synthesis, it is still unclear whether and how mammalian ORMDL3 regulates sphingolipid metabolism and whether altered sphingolipid synthesis would be causally related to asthma risk. Objective: We sought to examine the in vivo role of ORMDL3 in sphingolipid metabolism and allergic asthma. Methods: Ormdl3-LacZ reporter mice, gene-deficient Ormdl3(-/-) mice, and overexpressing Ormdl3(Tg/wt) mice were exposed to physiologically relevant aeroallergens, such as house dust mite (HDM) or Alternaria alternata, to induce experimental asthma. Mass spectrometry-based sphingolipidomics were performed, and airway eosinophilia, T(H)2 cytokine production, immunoglobulin synthesis, airway remodeling, and bronchial hyperreactivity were measured. Results: HDM challenge significantly increased levels of total sphingolipids in the lungs of HDM-sensitized mice compared with those in control mice. In Ormdl3(Tg/wt) mice the allergen-induced increase in lung ceramide levels was significantly reduced, whereas total sphingolipid levels were not affected. Conversely, in liver and serum, levels of total sphingolipids, including ceramides, were increased in Ormdl3(-/-) mice, whereas they were decreased in Ormdl3(Tg/wt) mice. This difference was independent of allergen exposure. Despite these changes, all features of asthma were identical between wildtype, Ormdl3(Tg/wt), and Ormdl3(-/-) mice across several models of experimental asthma. Conclusion: ORMDL3 regulates systemic ceramide levels, but genetically interfering with Ormdl3 expression does not result in altered experimental asthma