Biology subjects

Shinko, D.

Publications and source records attributed to Shinko, D..

5 recordsLinked to original sources

bioRxiv2026

Azacitidine Response in Myelodysplastic Syndromes is Marked by NK-like CD8 T-Cell Expansion and CXCL12+ Reticular Cell Remodeling

Myelodysplastic syndromes (MDS) are driven by somatic mutations in hematopoietic stem and progenitor cells (HSPCs), leading to clonal expansion and ineffective hematopoiesis. Hypomethylating agents (HMAs; azacitidine or decitabine) are the standard of care for higher-risk MDS. However, their effects on the bone marrow (BM) microenvironment, and the extent to which these changes correlate with clinical response, remain poorly understood. We performed longitudinal analyses of BM aspirates, trephine biopsies, and peripheral blood samples from MDS patients treated with azacitidine in a clinical trial (NCT03493646), integrating CyTOF, 5' single-cell RNA and TCR sequencing, plasma proteomics, and multiplex immunofluorescence microscopy to characterize changes associated with azacitidine response. Clinical responders showed expansion of GzmBCD56CD8 T cells together with increased type I and type II interferon signaling within the T-cell compartment. Responders also exhibited marked alterations in circulating platelet- and myeloid-derived factors with the potential to remodel the BM niche. Spatial analyses revealed expansion of "neighborhoods" enriched for CXCL12-abundant reticular cells and CD8 T cells in responders, whereas HSPC-enriched neighborhoods were largely unchanged. In contrast, several HSPC-enriched neighborhoods expanded in non-responders. These microenvironmental changes were accompanied by evidence of enhanced myelopoiesis in clinical responders. Our findings support a model in which azacitidine response extends beyond direct effects on malignant hematopoietic cells to involve coordinated remodeling of the BM microenvironment which may be reinforced by platelet- and myeloid-derived signals that establish a feed-forward circuit promoting productive hematopoiesis. One Sentence SummaryLongitudinal bone marrow profiling reveals stromal and immune correlates of azacitidine response in myelodysplastic syndrome.

cancer biology↗

bioRxiv2026

CD96-mediated internalisation of ligand CD155 as a novel mechanism for immune regulation

CD96 is a member of the immunoglobulin superfamily including TIGIT and CD226 that bind to a shared ligand, CD155. This family of co-receptors and its ligands are dysregulated in various autoimmune conditions and cancers, highlighting therapeutic potential. While TIGIT and CD226 are recognised co-inhibitory and co-stimulatory receptors respectively, the function of CD96 remains incompletely defined. Here, we assessed CD96-CD155 interaction and downstream trafficking to define a novel CD96 mechanism of action. Using primary human T cells and engineered cell models, we demonstrate that CD96 mediates uptake and internalisation of both soluble and cell-associated CD155. CRISPR-Cas9-mediated receptor knockout in primary human T cells revealed that CD155 uptake was uniquely dependent on CD96, but not TIGIT or CD226, identifying CD96 as the dominant mediator of CD155 internalisation in human T cells. This uptake process was dependent on active receptor cycling, which was partially facilitated by the CD96 cytoplasmic domain. Furthermore, CD96 variant 2 exhibited enhanced ligand binding and uptake efficiency compared with variant 1. Mechanistically, internalised CD155 trafficked to lysosomal compartments and associated with autophagy-related proteins, consistent with degradative processing. These findings reveal a previously unrecognised mechanism by which CD96 may regulate ligand availability through ligand internalisation and trafficking, with potential implications for T cell function and immune regulation. This process may promote changes in the immunoregulatory balance and could inform new targeted therapeutic development.

immunology↗

bioRxiv2024

Treg fitness as a biomarker for disease activity in Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is an autoimmune condition characterised by persistent flares of joint inflammation. However, no reliable biomarker exists to predict the erratic disease course. Normally, regulatory T cells (Tregs) maintain immune tolerance, with altered Tregs associated with autoimmunity. Treg signatures have shown promise in monitoring other autoimmune conditions, therefore a Treg gene and/or protein signature could offer novel biomarker potential for predicting disease activity in JIA. Machine learning on our nanoString Treg gene signature on peripheral blood (PB) Tregs generated a model to distinguish active JIA (active joint count, AJC[≥]1) Tregs from healthy controls (HC, AUC=0.9875). Biomarker scores from this model successfully differentiated inactive (AJC=0) from active JIA PB Tregs. Moreover, scores correlated with clinical activity scores (cJADAS), and discriminated subclinical disease (AJC=0, cJADAS[≥]0.5) from remission (AUC=0.8980, Sens=0.8571, Spec= 0.8571). To investigate altered Treg fitness in JIA by protein expression, we utilised spectral flow cytometry and unbiased analysis. Three Treg clusters were increased in active JIA PB, including CD226highCD25low effector-like Tregs and CD39-TNFR2-Helioshigh, while a 4-1BBlowTIGITlowID2intermediate Treg cluster predominated in inactive JIA PB (AJC=0). The ratio of these Treg clusters correlated to cJADAS, and higher ratios could predict inactive individuals that flared by 6-month follow-up. Thus, we demonstrate altered Treg signatures and subsets as an important factor, and useful biomarker, for disease progression versus remission in JIA, revealing genes and proteins important in Treg fitness. Ultimately, PB Treg fitness measures could serve as routine biomarkers to guide disease and treatment management to sustain remission in JIA.

immunology↗

bioRxiv2023

The immune landscape of the inflamed joint defined by spectral flow cytometry

Cellular phenotype and function are altered in different microenvironments. For targeted therapies it is important to understand site-specific cellular adaptations. Juvenile Idiopathic Arthritis (JIA) is characterised by joint inflammation, with frequent inadequate treatment responses. To comprehensively assess the inflammatory immune landscape, we designed a 37-parameter spectral flow cytometry panel delineating mononuclear cells from JIA synovial fluid (SF), compared to JIA and healthy control blood. Synovial monocytes and NK cells lack the Fc-receptor CD16, suggesting antibody-mediated targeting may be ineffective. B cells and DCs, both in small frequencies in SF, undergo maturation with high 4-1BB, CD71, CD39 expression, supporting T cell activation. SF effector and regulatory T cells were highly active with newly described co-receptor combinations that may alter function, and suggestion of metabolic reprogramming via CD71, TNFR2 and PD-1. Most SF effector phenotypes, as well as an identified CD4-Foxp3+ T cell population, were restricted to the inflamed joint, yet specific SF-predominant Treg (CD4+Foxp3+) subpopulations were increased in blood of active but not inactive JIA, suggesting possible recirculation and loss of immunoregulation at distal sites. This first comprehensive dataset of the site-specific inflammatory landscape at protein level will inform functional studies and the development of targeted therapeutics to restore immunoregulatory balance and achieve remission in JIA. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/569010v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@66c213org.highwire.dtl.DTLVardef@4f99b5org.highwire.dtl.DTLVardef@1f475bborg.highwire.dtl.DTLVardef@5d533f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre

As the size and complexity of high-dimensional cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large high-dimensional datasets, generated by flow cytometry, mass cytometry (CyTOF), or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing (scRNAseq) or high-dimensional imaging technologies, such as Imaging Mass Cytometry (IMC). The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

immunology↗