Anna Lorenc

Adjuvanted influenza-H1N1 vaccination reveals lymphoid signatures of age-dependent early responses and of clinical adverse events

Adjuvanted vaccines afford invaluable protection against disease, and the molecular and cellular changes they induce offer direct insight into human immunobiology. Here we show that within 24 h of receiving adjuvanted swine flu vaccine, healthy individuals made expansive, complex molecular and cellular responses that included overt lymphoid as well as myeloid contributions. Unexpectedly, this early response was subtly but significantly different in people older than ∼35 years. Wide-ranging adverse clinical events can seriously confound vaccine adoption, but whether there are immunological correlates of these is unknown. Here we identify a molecular signature of adverse events that was commonly associated with an existing B cell phenotype. Thus immunophenotypic variation among healthy humans may be manifest in complex pathophysiological responses.

Candidate driver genes involved in genome maintenance and DNA repair in Sézary syndrome

Sézary syndrome (SS) is a leukemic variant of cutaneous T-cell lymphoma (CTCL) and represents an ideal model for study of T-cell transformation. We describe whole-exome and single-nucleotide polymorphism array-based copy number analyses of CD4(+) tumor cells from untreated patients at diagnosis and targeted resequencing of 101 SS cases. A total of 824 somatic nonsynonymous gene variants were identified including indels, stop-gain/loss, splice variants, and recurrent gene variants indicative of considerable molecular heterogeneity. Driver genes identified using MutSigCV include POT1, which has not been previously reported in CTCL; and TP53 and DNMT3A, which were also identified consistent with previous reports. Mutations in PLCG1 were detected in 11% of tumors including novel variants not previously described in SS. This study is also the first to show BRCA2 defects in a significant proportion (14%) of SS tumors. Aberrations in PRKCQ were found to occur in 20% of tumors highlighting selection for activation of T-cell receptor/NF-κB signaling. A complex but consistent pattern of copy number variants (CNVs) was detected and many CNVs involved genes identified as putative drivers. Frequent defects involving the POT1 and ATM genes responsible for telomere maintenance were detected and may contribute to genomic instability in SS. Genomic aberrations identified were enriched for genes implicated in cell survival and fate, specifically PDGFR, ERK, JAK STAT, MAPK, and TCR/NF-κB signaling; epigenetic regulation (DNMT3A, ASLX3, TET1-3); and homologous recombination (RAD51C, BRCA2, POLD1). This study now provides the basis for a detailed functional analysis of malignant transformation of mature T cells and improved patient stratification and treatment.

β‐cell specific T‐lymphocyte response has a distinct inflammatory phenotype in children with Type 1 diabetes compared with adults

To examine the hypothesis that the quality, magnitude and breadth of helper T‐lymphocyte responses to β cells differ in Type 1 diabetes according to diagnosis in childhood or adulthood.

Autoreactive T effector memory differentiation mirrors β cell function in type 1 diabetes

In type 1 diabetes, cytotoxic CD8+ T cells with specificity for &bgr; cell autoantigens are found in the pancreatic islets, where they are implicated in the destruction of insulin-secreting &bgr; cells. In contrast, the disease relevance of &bgr; cell–reactive CD8+ T cells that are detectable in the circulation, and their relationship to &bgr; cell function, are not known. Here, we tracked multiple, circulating &bgr; cell–reactive CD8+ T cell subsets and measured &bgr; cell function longitudinally for 2 years, starting immediately after diagnosis of type 1 diabetes. We found that change in &bgr; cell–specific effector memory CD8+ T cells expressing CD57 was positively correlated with C-peptide change in subjects below 12 years of age. Autoreactive CD57+ effector memory CD8+ T cells bore the signature of enhanced effector function (higher expression of granzyme B, killer-specific protein of 37 kDa, and CD16, and reduced expression of CD28) compared with their CD57– counterparts, and network association modeling indicated that the dynamics of &bgr; cell–reactive CD57+ effector memory CD8+ T cell subsets were strongly linked. Thus, coordinated changes in circulating &bgr; cell–specific CD8+ T cells within the CD57+ effector memory subset calibrate to functional insulin reserve in type 1 diabetes, providing a tool for immune monitoring and a mechanism-based target for immunotherapy.

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry

Motivation Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable. Results flowLearn is a semi‐supervised approach for the quality‐checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state‐of‐the‐art datasets, our tool achieves Symbol‐measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training. Symbol. No Caption available. Availability and implementation FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material.

T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes

Defects in T cell receptor (TCR) repertoire are proposed to predispose to autoimmunity. Here we show, by analyzing >2 × 108TCRB sequences of circulating naive, central memory, regulatory and stem cell-like memory CD4+ T cell subsets from patients with type 1 diabetes and healthy donors, that patients have shorter TCRB complementarity-determining region 3s (CDR3), in all cell subsets, introduced by increased deletions/reduced insertions during VDJ rearrangement. High frequency of short CDR3s is also observed in unproductive TCRB sequences, which are not subjected to thymic culling, suggesting that the shorter CDR3s arise independently of positive/negative selection. Moreover, TCRB CDR3 clonotypes expressed by autoantigen-specific CD4+ T cells are shorter compared with anti-viral T cells, and with those from healthy donors. Thus, early events in thymic T cell development and repertoire generation are abnormal in type 1 diabetes, which suggest that short CDR3s increase the potential for self-recognition, conferring heightened risk of autoimmune disease.T cell receptors are generated by somatic gene recombination, and are normally selected against autoreactivity. Here the authors show that CD4 T cells from patients with autoimmune type 1 diabetes have shorter TCRβ sequences, broader repertoire diversity, and more repertoire sharing than those from healthy individuals.

High throughput automated analysis of big flow cytometry data

The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).

Erratum. Blood and Islet Phenotypes Indicate Immunological Heterogeneity in Type 1 Diabetes. Diabetes 2014;63:3835-3845

In the article listed above, there are two errors in the research design and methods section. In the section with the heading “Studies on Islet-Infiltrating Leukocytes,” the antibody listed as #M0701 should be attributed to Dako and not to Abcam and the Abcam rabbit anti-CD8 catalogue number should read #ab4055 and not #GR404-4. The online version reflects these changes.