Biology subjects

Schupp, J.

Publications and source records attributed to Schupp, J..

4 recordsLinked to original sources

bioRxiv2025

Benchmarking cell-type deconvolution in cross-platform transcriptomic data

BackgroundTranscriptomic data from diverse measurement technologies are widely used to study tissue heterogeneity. Cell-type deconvolution, which resolves mixed transcriptomic signals into cellular components, is a key analytical approach. However, achieving accurate deconvolution across platforms remains challenging due to platform-specific experimental and technological biases. ResultsWe systematically benchmarked deconvolution performance using real-world cross-platform datasets and simulated data modeling distinct technological features. Our analyses provide practical guidelines for experimental design and promote more robust and comparable cross-platform transcriptomic analyses. ConclusionLog-normal regression methods such as SpatialDecon demonstrated the most reliable and consistent performance across both simulated and experimental settings, establishing a robust framework for accurate cross-platform transcriptomic deconvolution. Moreover, our results highlight potential caveats in deconvolution predictions arising from specific experimental conditions, providing guidance for more informed experimental design and interpretation Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/690141v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@585cbdorg.highwire.dtl.DTLVardef@13088c7org.highwire.dtl.DTLVardef@163ea75org.highwire.dtl.DTLVardef@b5ccf7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Glioma-associated tertiary lymphoid structures are sites of lymphocyte clonal expansion and plasma cell formation

Adult-type diffuse gliomas, the most common primary brain tumors, pose significant clinical challenges due to limited treatment options, restricted anti-tumor immune response and dismal patient prognosis. In this study, we elucidate the immunological function and clinical relevance of intra-tumoral tertiary lymphoid structures (TLS) in adaptive anti-glioma immunity. We conducted a comprehensive, unbiased analysis of lymphoid aggregation in 642 gliomas using a multi-modal approach that combines RNA sequencing with spatial transcriptome and proteome profiling. Our findings reveal that TLS are present in 15% of tumors and correlate with improved overall survival. Gliomas with TLS exhibit a remodeled perivascular space, marked by transcriptional upregulation and spatial redistribution of collagens associated with barrier functions. Furthermore, we demonstrate that TLS maturation into sites of dynamic adaptive immune responses, characterized by clonal T and B cell expansion and IgA+ and IgG+ plasma-cell formation, is driven by efficient early T cell recruitment to the perivascular space.

immunology↗

bioRxiv2021

CINS: Cell Interaction Network inference from Single cell expression data

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging human dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

bioinformatics↗

bioRxiv2020

Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory Receptors in Human T cells

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Type 1 interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and may facilitate induction of T cell exhaustion in chronic viral infection1,2. Here we show that IFN-I regulates co-inhibitory receptors expression on human T cells, inducing PD-1/TIM-3/LAG-3 while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses enabled the construction of dynamic transcriptional regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors on human primary T cells revealed both canonical and non-canonical IFN-I transcriptional regulators, and identified unique regulators that control expression of co-inhibitory receptors. To provide direct in vivo evidence for the role of IFN-I on co-inhibitory receptors, we then performed single cell RNA-sequencing in subjects infected with SARS-CoV-2, where viral load was strongly associated with T cell IFN-I signatures. We found that the dynamic IFN-I response in vitro closely mirrored T cell features with acute IFN-I linked viral infection, with high LAG3 and decreased TIGIT expression. Finally, our gene regulatory network identified SP140 as a key regulator for differential LAG3 and TIGIT expression. The construction of co-inhibitory regulatory networks induced by IFN-I with identification of unique transcription factors controlling their expression may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

immunology↗