Biology subjects

Shin, M.-G.

Publications and source records attributed to Shin, M.-G..

4 recordsLinked to original sources

bioRxiv2023

Long COVID manifests with T cell dysregulation, inflammation, and an uncoordinated adaptive immune response to SARS-CoV-2

Long COVID (LC), a type of post-acute sequelae of SARS-CoV-2 infection (PASC), occurs after at least 10% of SARS-CoV-2 infections, yet its etiology remains poorly understood. Here, we used multiple "omics" assays (CyTOF, RNAseq/scRNAseq, Olink) and serology to deeply characterize both global and SARS-CoV-2-specific immunity from blood of individuals with clear LC and non-LC clinical trajectories, 8 months following infection and prior to receipt of any SARS-CoV-2 vaccine. Our analysis focused on deep phenotyping of T cells, which play important roles in immunity against SARS-CoV-2 yet may also contribute to COVID-19 pathogenesis. Our findings demonstrate that individuals with LC exhibit systemic inflammation and immune dysregulation. This is evidenced by global differences in T cell subset distribution in ways that imply ongoing immune responses, as well as by sex-specific perturbations in cytolytic subsets. Individuals with LC harbored increased frequencies of CD4+ T cells poised to migrate to inflamed tissues, and exhausted SARS-CoV-2-specific CD8+ T cells. They also harbored significantly higher levels of SARS-CoV-2 antibodies, and in contrast to non-LC individuals, exhibited a mis-coordination between their SARS-CoV-2-specific T and B cell responses. RNAseq/scRNAseq and Olink analyses similarly revealed immune dysregulatory mechanisms, along with non-immune associated perturbations, in individuals with LC. Collectively, our data suggest that proper crosstalk between the humoral and cellular arms of adaptive immunity has broken down in LC, and that this, perhaps in the context of persistent virus, leads to the immune dysregulation, inflammation, and clinical symptoms associated with this debilitating condition.

immunology↗

bioRxiv2022

RMeDPower for Biology: guiding design, experimental structure and analyses of repeated measures data for biological studies

Lack of experimental reproducibility has plagued efforts to understand biology at both basic biomedical and preclinical levels. The cause is often improperly powered experiments and the use of inadequate statistical tools. To overcome these problems, we developed RMeDPower2, a complete, user-friendly package of tools in R that will allow scientists that are not deeply familiar with statistical analyses to predict the scope and size of biological data they need when conducting experiments with a repeated measures design. RMeDPower2 is based on Generalized Linear Mixed Effects Models (GLMM), which are better suited to the statistical analysis of these experiments than ANOVA or t-tests. We illustrate the use of RMeDPower2 and compare it to t- test for power calculations, using our own pilot studies of iPSC-derived motor neurons (iMNs) from sporadic ALS (sALS) patients versus healthy controls. We report that sALS iMNs display reduced numbers of soma- emanating processes compared to control iMNs using RMeDPower2. We expect RMeDPower2 to find applications far beyond cell assays, from single-cell RNAseq experiments to brain slice electrophysiology or animal behavior. MotivationThe lack of rigor and reproducibility in biomedical research has caused a crisis that has been highlighted in the popular literature and has become a focus for the National Institutes of Health1-3. It has been estimated that the majority of published empirical observations cannot be reproduced4-9, rendering nearly futile any effort to build on these observations to further our understanding of basic biological mechanisms or design effective therapeutic approaches. Further, the resources and time spent attempting to reproduce findings from low-quality or incorrectly acquired data are estimated to cost the global scientific community about 200 billion dollars per year10. The root cause lies in experimental designs that are not structured or powered adequately for conclusive statistical analyses. Since all biomedical researchers cannot be expected to have a deep knowledge of statistics or easy access to trained statisticians, tools are desperately needed to help them check the design of their experiments and apply adequate statistical power estimation. Not only could this improve our confidence in scientific outcomes, it could help make biological experiments more time-efficient and cost-effective. For example, if a researcher could estimate how many experiments should be performed and how many cell lines, animals or tissue samples should be collected to achieve sufficient statistical power to test their hypothesis, they may adjust their experimental design to fit their time or budgetary constraints without jeopardizing the quality of their findings. Another source of scientific errors comes from technologies such as scRNA-seq, whose advances are leading to a rapid increase in studies involving so-called "pseudo-replication", which treats non-independent measures as if they were independent. For example, carrying out multiple measurements on a single sample instead of using separate, independent samples would represent non-independent replication. The risk of pseudo-replication11 (illustrated further below), can be remedied by the implementation of rigorous statistical methods that apply to all aspects of the data arising from such designs.

cell biology↗

bioRxiv2022

Deep Immunophenotyping Reveals Endometriosis is Marked by Dysregulation of the Mononuclear Phagocytic System in Endometrium and Peripheral Blood

BackgroundEndometriosis is a chronic, estrogen-dependent disorder where inflammation contributes to disease-associated symptoms of pelvic pain and infertility. Immune dysfunction includes insufficient immune lesion clearance, a pro-inflammatory endometrial environment, and systemic inflammation. Comprehensive understanding of endometriosis immune pathophysiology in different hormonal milieu and disease severity has been hampered by limited direct characterization of immune populations in endometrium, blood, and lesions. Simultaneous deep phenotyping at single cell resolution of complex tissues has transformed our understanding of the immune system and its role in many diseases. Herein, we report mass cytometry and high dimensional analyses to study immune cell phenotypes, abundance, activation states, and functions in endometrium and blood of women with and without endometriosis in different cycle phases and disease stages. MethodsA case-control study was designed. Endometrial biopsies and blood (n=60 total) were obtained from women with (n=20, n=17, respectively) and without (n=14, n=9) endometriosis in the proliferative and secretory cycle phases of the menstrual cycle. Two mass cytometry panels were designed; one broad panel and one specific for mononuclear phagocytic cells (MPC), and all samples were multiplexed to characterize both endometrium and blood immune composition at unprecedented resolution. We combined supervised and unsupervised analyses to finely define the immune cell subsets with an emphasis on MPC. Then, association between cell types, protein expression, disease status, and cycle phase were performed. ResultsThe broad panel highlighted a significant modification of MPC in endometriosis; thus, they were studied in detail with an MPC-focused panel. Endometrial CD91+ macrophages overexpressed SIRP (phagocytosis inhibitor) and CD64 (associated with inflammation) in endometriosis, and they were more abundant in mild versus severe disease. In blood, classical and intermediate monocytes were less abundant in endometriosis, whereas plasmacytoid dendritic cells and non-classical monocytes were more abundant. Non-classical monocytes were higher in severe versus mild disease. ConclusionsA greater inflammatory phenotype and decreased phagocytic capacity of endometrial macrophages in endometriosis are consistent with defective clearance of endometrial cells shed during menses and in tissue homeostasis, with implications in endometriosis pathogenesis and pathophysiology. Different proportions of monocytes and plasmacytoid dendritic cells in blood from endometriosis suggest systemically aberrant functionality of the myeloid system opening new venues for the study of biomarkers and therapies for endometriosis.

immunology↗

bioRxiv2021

Deep Phenotypic Analysis of Blood and Lymphoid T and NK Cells from HIV+ Controllers and Non-Controllers

T and natural killer (NK) cells are effector cells with key roles in anti-HIV immunity, including in lymphoid tissues, the major site of HIV persistence. In this study, we used 42-parameter CyTOF to conduct deep phenotyping of paired blood- and lymph node (LN)-derived T and NK cells from three groups of HIV+ aviremic individuals: elite controllers, and antiretroviral therapy (ART)-suppressed individuals who had started therapy during chronic vs. acute infection, the latter of which is associated with better outcomes. We found that acute-treated individuals are enriched for specific subsets of T and NK cells, including blood-derived CD56-CD16+ NK cells previously associated with HIV control, and LN-derived CD4+ T follicular helper cells with heightened expansion potential. An in-depth comparison of the features of the cells from blood vs. LNs of individuals from our cohort revealed that T cells from blood were more activated than those from LNs. By contrast, LNs were enriched for follicle-homing CXCR5+ CD8+ T cells, which expressed increased levels of inhibitory receptors and markers of survival and proliferation as compared to their CXCR5-counterparts. In addition, a subset of memory-like CD56brightTCF1+ NK cells was enriched in LNs relative to blood. These results together suggest unique T and NK cell features in acute-treated individuals, and highlight the importance of examining effector cells not only in blood but also the lymphoid tissue compartment, where the reservoir mostly persists, and where these cells take on distinct phenotypic features.

immunology↗