Biology subjects

DePasquale, E. A.

Publications and source records attributed to DePasquale, E. A..

9 recordsLinked to original sources

bioRxiv2025

MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis

Imaging-based spatially resolved transcriptomics (SRT) technologies, such as 10X Xenium, MERSCOPE, and CosMx, have revolutionized our ability to study gene expression within the spatial context of tissues at single-cell resolution. The acquisition of such data relies heavily on cell segmentation algorithms, which often produce imperfect boundaries, leading to transcript misassignment. These misassignments can significantly affect downstream analyses, including cell type identification, differential expression analysis, cell-cell communication, and RNA localization. We present MisTIC (Missegmented Transcript Inference Correction), a variational Bayesian model designed to correct transcript misassignment errors without requiring resegmentation. In benchmarking analyses using synthetic data with simulated transcript misassignment, MisTIC demonstrated high sensitivity and specificity in removing misassigned transcripts. In real data applications, MisTIC effectively enhances cell type identification, reduces ambiguity in differential expression analysis, and improves the detection of cell-cell communication. Furthermore, RNA localization analysis based on data corrected by MisTIC revealed that, in T cells located near cancer-associated fibroblasts compared to those farther away, genes involved in T cell activation, inflammation, and cytotoxicity were depleted from cytoplasmic regions despite not being differentially expressed between the two T cell subsets. In conclusion, MisTIC is a powerful tool for correcting transcript misassignment in SRT data. It not only improves the accuracy of routine analyses but also enables novel investigations that provide deeper insights into the dynamics of gene expression.

bioinformatics↗

bioRxiv2025

Co-Stimulatory Blockade Prevents Intragraft Accrual of Class-Switched, Activated B Cells Despite Failing to Prevent T-Cell Mediated Rejection

Previously, we defined the transcriptomes and clonality of intragraft CD8+ T cell during renal allograft rejection. Here, using single cell RNA sequencing (scRNAseq), we investigated non-CD8+ immune cells during T-cell-mediated rejection (TCMR) under different maintenance immunosuppression (mIS) regimens: tacrolimus, and co-stimulatory blockade (CoB) with belatacept (CTLA4-Ig) or iscalimab (anti-CD40). Myeloid cells comprised of DCs, monocytes and macrophages whose proportion and gene expression were similar between mIS regimens. Given their transcriptiomic similarities, we analyzed publicly-available scRNAseq/CITEseq datasets as well as immunofluorescence staining to resolve independent subpopulations of {gamma}/{delta} T cells and NK cells. Intragraft B cells consisted of clusters of naive, plasmablast, and class-switched B (BCS) cells, with the latter being diminished in CoB mIS. Intragraft CD4+ T cells consisted of FoxP3+ regulatory (Treg), exhausted, Th17, and CXCL13+ peripheral helper (Tph) cells whose proportions differed based in mIS, and the latter two had increased clonal expansion. Notably, cell-cell communication analysis indicated Th17 and Tph cell interactions with BCS cells in tacrolimus, but not CoB, samples. Thus, although failing to prevent TCMR, CoB mIS modulates the accrual of CD4+ T cells and inhibits the intragraft accrual of BCS cells, possibly reflective of clinical observations of less chronic antibody-mediated rejection under CoB mIS.

immunology↗

bioRxiv2025

Oncogenic Role of Aberrant EZH2 in Hepatoblastoma

Hepatoblastoma (HB) is the most common pediatric liver malignancy. However, its cellular origin and molecular drivers remain poorly defined. Using single-nuclear RNA sequencing (snRNA-seq), we identified a proliferative, hepatocyte-derived tumor cell population (cycling HepT) enriched for Enhancer of Zeste Homolog 2 (EZH2) expression, particularly in the aggressive embryonal subtype. Integrative genomic and transcriptomic profiling confirmed EZH2 overexpression. Disruption of the PRC2 complex was evident through mislocalization and reduced expression of SUZ12, a core component. EZH2 overexpression correlated with upregulation of mitotic regulators such as AURKB and Ki67 in human HB gene expression analysis as compared to background liver. Targeted sequencing identified variants of uncertain significance in EZH2 and SUZ12 in 11 of 11 patient tumors. Pharmacologic inhibition of EZH2 with EPZ-6438 reduced proliferation and sensitized HB cells to cisplatin through gene regulation, potentially modulating platinum accumulation both in vitro and in vivo. In summary, EZH2 promotes HB progression through epigenetic silencing and noncanonical signaling pathways. These findings support EZH2s contribution to HB pathogenesis, therefore identifying it as a novel therapeutic target. STATEMENT OF SIGNIFICANCEEZH2 is enriched in hepatoblastoma, suggesting its role in early tumorigenesis. Its inhibition, combined with cisplatin, produces synergistic anti-tumor effects, supporting EZH2 as both an oncogenic and therapeutic target.

cancer biology↗

bioRxiv2024

Defining the T cell transcriptional landscape in pediatric liver transplant rejection at single cell resolution

Acute cellular rejection (ACR) affects >80% of pediatric liver transplant recipients within 5 years, and late ACR is associated with graft failure. Traditional anti-rejection therapy for late ACR is ineffective and has remained unchanged for six decades. Although CD8+ T cells promote late ACR, little has been done to define their specificity and gene expression. Here, we used single-cell sequencing and immune repertoire profiling (10X Genomics) on 30 cryopreserved 16G liver biopsies from 14 patients (5 pre-transplant or with no ACR, 9 with ACR). We identified expanded intragraft CD8+ T cell clonotypes (CD8EXP) and their gene expression profiles in response to anti-rejection treatment. Notably, we found that expanded CD8+ clonotypes (CD8EXP) bore markers of effector and CD56hiCD161- NK-like T cells, retaining their clonotype identity and phenotype in subsequent biopsies from the same patients despite histologic ACR resolution. CD8EXP clonotypes localized to portal infiltrates during active ACR, and persisted in the lobule after histologic ACR resolution. CellPhoneDB analysis revealed differential crosstalk between KC and CD8EXP during late ACR, with activation of the LTB-LTBR pathway and downregulation of TGF{beta} signaling. Therefore, persistently-detected intragraft CD8EXP clones remain active despite ACR treatment and may contribute to long-term allograft fibrosis and failure of operational tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/582173v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@50a954org.highwire.dtl.DTLVardef@19ccc02org.highwire.dtl.DTLVardef@115be16org.highwire.dtl.DTLVardef@72a858_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Single-cell Multiomics Reveals Clonal T-cell Expansions and Exhaustion in Blastic Plasmacytoid Dendritic Cell Neoplasm

The immune system represents a major barrier to cancer progression, driving the evolution of immunoregulatory interactions between malignant cells and T-cells in the tumor environment. Blastic plasmacytoid dendritic cell neoplasms (BPDCN), a rare acute leukemia with plasmacytoid dendritic cell (pDC) differentiation, provides a unique opportunity to study these interactions. pDCs are key producers of interferon alpha (IFNA) that play an important role in T-cell activation at the interface between the innate and adaptive immune system. To assess how uncontrolled proliferation of malignant BPDCN cells affects the tumor environment, we catalog immune cell heterogeneity in the bone marrow (BM) of five healthy controls and five BPDCN patients by analyzing 52,803 single-cell transcriptomes, including 18,779 T-cells. We test computational techniques for robust cell type classification and find that T-cells in BPDCN patients consistently upregulate interferon alpha (IFNA) response and downregulate tumor necrosis factor alpha (TNFA) pathways. Integrating transcriptional data with T-cell receptor sequencing via shared barcodes reveals significant T-cell exhaustion in BPDCN that is positively correlated with T-cell clonotype expansion. By highlighting new mechanisms of T-cell exhaustion and immune evasion in BPDCN, our results demonstrate the value of single-cell multiomics to understand immune cell interactions in the tumor environment.

immunology↗

bioRxiv2020

KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

MotivationPhosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks. ResultsWe present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases. Availability and implementationKRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA. Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Identification and Removal of Doublets with DoubletDecon

Retention of multiplet captures in single-cell RNA-sequencing (scRNA-seq) data can hinder identification of discrete or transitional cell populations and associated marker genes. To overcome this challenge, we created DoubletDecon to identify and remove doublets, multiplets of two cells, by using a combination of deconvolution to identify putative doublets and analyses of unique gene expression. Here we provide the protocol for running DoubletDecon on scRNA-seq data. For complete details on the use of this protocol, please see DePasquale et al. (2019) (https://doi.org/10.1016/j.celrep.2019.09.082). GRAPHICAL ABSTRACT O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗

bioRxiv2020

Transcriptional profile of pyramidal neurons in chronic schizophrenia reveals lamina-specific dysfunction of neuronal immunity

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed over-represented groups of gene sets in schizophrenia, particularly in immunity and synapse related pathways in pyramidal neurons, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, post-synaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. By comparing our differential expression gene profiles with 51 antipsychotic treatment datasets, we demonstrated that our results were not influenced by antipsychotic treatment of our subjects. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

neuroscience↗

bioRxiv2018

CellHarmony: Cell-level matching and comparison of single-cell transcriptomes

To understand the molecular pathogenesis of human disease, precision analyses to define molecular alterations within (and between) disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell states. We note that disease-associated perturbations usually retain cellular-identity programs (core genes), providing an appropriate reference for secondary comparison analyses. Thus, we created cellHarmony, an integrated solution for the unsupervised analysis and classification of cell types from diverse scRNA-Seq datasets. cellHarmony is an automated and easy-to-use tool that efficiently matches single-cell transcriptomes using a community clustering and alignment strategy. Utilizing core genes and community clustering to reveal disease and cell-state systems-level insights overcomes bias toward donor and disease effects that can be imposed by joint-alignment approaches. Moreover, cellHarmony directly compares cell frequencies and gene expression in a cell-type-specific manner, then produces a holistic representation of these differences across potentially dozens of cell populations and impacted regulatory networks. Using this approach, we identify gene regulatory programs that are selectively impacted in distinct hematopoietic and heart cell populations that suggest novel disease mechanisms and drug targets. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex diseases.

bioinformatics↗