Adoptive transfer of allergen-expressing B cells prevents IgE-mediated allergy

IntroductionProphylactic strategies to prevent the development of allergies by establishing tolerance remain an unmet medical need. We previously reported that the transfer of autologous hematopoietic stem cells (HSC) expressing the major timothy grass pollen allergen, Phl p 5, on their cell surface induced allergen-specific tolerance in mice. In this study, we investigated the ability of allergen-expressing immune cells (dendritic cells, CD4+ T cells, CD8+ T cells, and CD19+ B cells) to induce allergen-specific tolerance in naive mice and identified CD19+ B cells as promising candidates for allergen-specific cell therapy.MethodsFor this purpose, CD19+ B cells were isolated from Phl p 5-transgenic BALB/c mice and transferred to naive BALB/c mice, pre-treated with a short course of rapamycin and an anti-CD40L antibody. Subsequently, the mice were subcutaneously sensitized three times at 4-week intervals to Phl p 5 and Bet v 1 as an unrelated control allergen. Allergen-expressing cells were followed in the blood to monitor molecular chimerism, and sera were analyzed for Phl p 5- and Bet v 1-specific IgE and IgG1 levels by RBL assay and ELISA, respectively. In vivo allergen-induced lung inflammation was measured by whole-body plethysmography, and mast cell degranulation was determined by skin testing.ResultsThe transfer of purified Phl p 5-expressing CD19+ B cells to naive BALB/c mice induced B cell chimerism for up to three months and prevented the development of Phl p 5-specific IgE and IgG1 antibody responses for a follow-up period of 26 weeks. Since Bet v 1 but not Phl p 5-specific antibodies were detected, the induction of tolerance was specific for Phl p 5. Whole-body plethysmography revealed preserved lung function in CD19+ B cell-treated mice in contrast to sensitized mice, and there was no Phl p 5-induced mast cell degranulation in treated mice.DiscussionThus, we demonstrated that the transfer of Phl p 5-expressing CD19+ B cells induces allergen-specific tolerance in a mouse model of grass pollen allergy. This approach could be further translated into a prophylactic regimen for the prevention of IgE-mediated allergy in humans

Nachweis seltener Zellpopulationen in Durchflusszytometriedaten mit kleinen Trainingsmengen

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersMaschinelles Lernen wird für die automatische Klassifizierung von Zellpopulationen in Durchflusszytometrie (FCM)-Daten eingesetzt, mit dem Ziel, die zeitaufwändige und subjektive manuelle Analyse zu ersetzen. Welche Techniken verwendet werden, hängt vom Feld und dem Ziel der FCM-Datenanalyse ab. Herausforderungen sind unter anderem der mehrdimensionale Datenraum mit potenziell Millionen von Messpunkten und der Anteil der zu erfassenden Zellpopulationen, der so gering wie nur 0,01% sein kann. Die vorkommenden Verteilungen der Zellpopulationen sind komplex und weisen eine hohe Variabilität zwischen den Proben auf. In dieser Arbeit wird der Random-Forest-Klassifikator im Detail betrachtet und in dem komplexen Klassifikationsproblem des Krebszellnachweises in FCM-Daten von Knochenmarksproben von Patienten mit akuter lymphoblastischer Leukämie der Vorläufer B-Zellen untersucht. Es wird evaluiert wie sich Merkmalstransformation und Dimensionsreduktion mittels unüberwachter Uniform-Manifold-Approximation-Projektion (UMAP) vor der Klassifikation mittels Random-Forest auf die Ergebnisse auswirken. Beide Ansätze werden mit einer auf Gaussian Mixture Models basierenden Methode verglichen, die speziell für diese Aufgabe entwickelt wurde. Alle drei Ansätze werden für verschiedene Trainingset-Größen auf öffentlich zugänglichen Datensätzen untersucht, mit dem Hintergrund, dass bei der automatisierten FCM-Datenanalysen eine begrenzte Verfügbarkeit von Trainingsdaten angetroffen werden kann. Schließlich werden verschiedene Methoden zur Dimensionsreduktion verglichen. Der vorgestellte Ansatz basierend UMAP und RF, erweist sich als überlegen in Bezug auf den mittleren F1-Score bei Trainingsets mit weniger als 34 Proben.Machine learning techniques are used for automated classification of cell populations in Flow Cytometry (FCM) data with the objective of superseding time-consuming and subjective manual gating. The techniques used depend on the field and the aim of the FCM data analysis. Challenges involved are the multi-dimensional observation space with potentially millions of observations and the proportion of cell populations to be detected as low as 0.01%. The naturally occurring distributions of cell populations are complex and exhibit a high inter-sample variability. In this thesis the standard, off-the-shelf random forest classifier is studied in detail and examined in the complex classification task of cancer cell detection in FCM data of bone marrow samples of precursor B acute lymphoblastic leukemia patients. The impact of feature transformation and dimension reduction with unsupervised uniform manifold approximation projection (UMAP) prior to classification is explored. Both approaches are compared to a state-of-the-art method based on Gaussian mixture models, which was specifically designed for this task. All three approaches are examined for a varying training set size on publicly available data sets, with the motivation of limited availability of training data that can be encountered in automated FCM data analysis. Finally, different dimension reduction methods are compared. The proposed semi-supervised approach based on UMAP and RF proves superior with respect to average F1-score on training sets with less than 34 samples6

UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia

Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F1-score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML

FCM marker importance for MRD assessment in T-Cell Acute Lymphoblastic Leukemia: An AIEOP-BFM-ALL-FLOW Study Group Report

Background: T-lineage acute lymphoblastic leukemia (T-ALL) accounts for about 15 % of pediatric and about 25 % of adult ALL cases. Minimal/measurable Residual Disease (MRD) assessed by Flow Cytometry (FCM) is an important prognostic indicator for risk stratification. In order to assess the MRD a limited number of antibodies directed against the most discriminative antigens must be selected. Methods: We propose a pipeline for evaluating the influence of different markers for cell population classification in FCM data. We use linear Support Vector Machine, fitted to each sample individually to avoid issues with patient and laboratory variations. The best separating hyperplane direction as well as the influence of omitting specific markers is considered. Results: 91 bone marrow samples of 43 pediatric T-ALL patients from 5 reference laboratories were analyzed by FCM regarding marker importance for blast cell identification using combinations of 8 different markers. For all laboratories, CD48 and CD99 were among the top 3 markers with strongest contribution to the optimal hyperplane, measured by median separating hyperplane coefficient size for all samples per center and timepoint (diagnosis, day15, day33). Conclusions: Based on the available limited set tested (CD3, CD4, CD5, CD7, CD8, CD45, CD48, CD99), our findings prove that CD48 and CD99 are useful markers for minimal residual disease (MRD) monitoring in T-ALL. The proposed pipeline can be applied for evaluation of other marker combinations in the future. This article is protected by copyright. All rights reserved

Incarceration history is associated with HIV infection among community-recruited people who inject drugs in Europe:a propensity-score matched analysis of cross- sectional studies

AimsWe measured the association between a history of incarceration and HIV positivity among people who inject drugs (PWID) across Europe.Design, Setting and ParticipantsThis was a cross-sectional, multi-site, multi-year propensity-score matched analysis conducted in Europe. Participants comprised community-recruited PWID who reported a recent injection (within the last 12 months).MeasurementsData on incarceration history, demographics, substance use, sexual behavior and harm reduction service use originated from cross-sectional studies among PWID in Europe. Our primary outcome was HIV status. Generalized linear mixed models and propensity-score matching were used to compare HIV status between ever- and never-incarcerated PWID.FindingsAmong 43 807 PWID from 82 studies surveyed (in 22 sites and 13 countries), 58.7% reported having ever been in prison and 7.16% (n = 3099) tested HIV-positive. Incarceration was associated with 30% higher odds of HIV infection [adjusted odds ratio (aOR) = 1.32, 95% confidence interval (CI) = 1.09–1.59]; the association between a history of incarceration and HIV infection was strongest among PWID, with the lowest estimated propensity-score for having a history of incarceration (aOR = 1.78, 95% CI = 1.47–2.16). Additionally, mainly injecting cocaine and/or opioids (aOR = 2.16, 95% CI = 1.33–3.53), increased duration of injecting drugs (per 8 years aOR = 1.31, 95% CI = 1.16–1.48), ever sharing needles/syringes (aOR = 1.91, 95% CI = 1.59–2.28) and increased income inequality among the general population (measured by the Gini index, aOR = 1.34, 95% CI = 1.18–1.51) were associated with a higher odds of HIV infection. Older age (per 8 years aOR = 0.84, 95% CI = 0.76–0.94), male sex (aOR = 0.77, 95% CI = 0.65–0.91) and reporting pharmacies as the main source of clean syringes (aOR = 0.72, 95% CI = 0.59–0.88) were associated with lower odds of HIV positivity.ConclusionsA history of incarceration appears to be independently associated with HIV infection among people who inject drugs (PWID) in Europe, with a stronger effect among PWID with lower probability of incarceration.</div