Biology subjects

Plevritis, S. K.

Publications and source records attributed to Plevritis, S. K..

10 recordsLinked to original sources

bioRxiv2025

MONTAGE: A Computation Framework to Identify Spatially Resolved Functional Enrichment Gradients in the Tissue Microenvironment via Spatial Communities

Single-cell spatial-omics has advanced our understanding of how tumor microenvironment contributes to cancer progression. However, most single-cell spatial-omics studies focus on cell types and neighborhoods, offering limited information about functional and clinical relevance of cellular organization. We introduce MONTAGE, a computational framework to reconstruct, functionally analyze, and identify clinically relevant cellular spatial communities (SCs). MONTAGE generates a gene signature matrix of SCs from integrated atlases that offer single-cell resolution with transcriptomics, and reconstructs tissue maps ("montages"), reflecting sequential SC compositional changes along spatial gradients of biological function enrichments. The MONTAGE signature matrix can also be used to deconvolve bulk and spot-based spatial transcriptomic data, and combining with clinical metadata, allows assessing SC clinical relevance. In a head and neck cancer study, MONTAGE reveals SCs enriched with malignant cells and granulocytes shifting to SCs enriched with macrophages and fibroblasts along gradient of epithelial-mesenchymal transition, and clinical prognostic significance of the SCs.

systems biology↗

bioRxiv2025

Oncogenic mutations disrupt lineage differentiation via epigenetic perturbations

Understanding lineage differentiation is an ongoing challenge in the field of stem cell research. We acquired eight gain-of-function (GOF) oncogenic mutations associated with DNA methylation alteration from the TCGA datasets. After introducing each oncogene into wild-type stem cells separately, we found that the mutant stem cells are difficult to reprogram to iPSCs and terminally differentiate to induced neuronal cells, and retain proliferation capacity in the process of differentiation. This findings suggests that oncogenic mutations impair the lineage differentiation. After analyzing the resulting DNA methylation profiles, we identified many novel lineage differentiation genes in mutant cells showing DNA methylation alteration compared with wild-type cells, which is consistent with their methylation states in low-grade gliomas (LGGs) compared with control brain. Collectively, our results indicate that oncogenic mutations in stem cells are associated with DNA methylation changes for genes related to multipotency maintenance, differentiation resistance, and tumorigenesis, therefore, displaying impairment in lineage commitment.

cell biology↗

bioRxiv2025

Spatial Profiling Reveals Equivalence-Derived Molecular Signatures of Brain Mimicry and Adaptation in Breast Cancer Brain Metastases

Brain metastases (BrMets), common for advanced-stage breast cancer patients, are associated with poor median survival and accompanied by severe neurologic decline. Halting the progression of breast cancer brain metastases (BCBMs) may require modulation of the tumor microenvironment (TME), yet little is known about the impact of the primary breast TME on brain tropism, or how, once there, metastatic breast cancer cells coexist with brain-resident cells (e.g., neurons and glia). Traditionally, studies in this space have focused on differential expression analysis, overlooking potential insights gained from investigating genes with equivalent expression between groups. This is particularly crucial in distant metastasis, where tumor cells may co-opt the transcriptional programs of the host organ (e.g., brain) to facilitate successful seeding and outgrowth. Prior to our work, no computational framework existed to determine biologically-relevant equivalent gene expression. To resolve molecular mechanisms of BCBM enabled by metastatic cancer cells and/or resident brain cells, we leveraged Nanostring GeoMx to perform spatially-resolved transcriptomic profiling on 235 patient-derived tissue cores from BCBM (including adjacent normal brain), primary invasive breast cancers, and normal (non-cancer) brain; analyzing 18,677 RNAs in 450 areas of interest (AOIs). We introduce the "Equivalent Expression Index" a highly specific and accurate algorithm that identifies statistically significant "Equivalently-Expressed Genes". This method facilitated the identification of molecular remodeling and mimicry genes within tissue-specific TMEs. By integrating differential expression analysis with the Equivalent Expression Index, we discovered multiple novel gene signatures associated with BCBM and primary tumor brain-metastatic potential. We demonstrate that the Equivalent Expression Index is a powerful tool to uncover shared gene expression programs representing the adaptation of metastatic cells and brain-resident cells to the BCBM microenvironment.

cancer biology↗

bioRxiv2023

Mapping ovarian cancer spatial organization uncovers immune evasion drivers at the genetic, cellular, and tissue level

Immune exclusion and evasion are central barriers to the success of immunotherapies and cell therapies in solid tumors. Here we applied single cell spatial and perturbational transcriptomics alongside clinical, histological, and genomic profiling to elucidate immune exclusion and evasion in high-grade serous tubo-ovarian cancer (HGSC). Using high-plex spatial transcriptomics we profiled more than 1.3 million cells from 95 tumors and 60 patients, revealing generalizable principles in HGSC tumor tissue organization. Our data demonstrates that effector T cells resist stroma-mediated trapping and sequestration. However, upon infiltration into the tumor, T cells, as well as Natural Killer (NK) cells, preferentially co-localize only with a subset of malignant cells that manifest a distinct transcriptional cell state. The latter consists of dozens of co-regulated genes and is repressed under various copy number alterations. Performing CRISPR Perturb-seq screens in ovarian cancer cells, we identified functionally diverse genetic perturbations - including knockout of the insulin sensing repressor PTPN1 and the epigenetic regulator ACTR8 - that de-repress the proposed immunogenic malignant cell state identified in patients and indeed sensitize ovarian cancer cells to T cell and NK cell cytotoxicity. Taken together, our study uncovered a profound connection between somatic genetic aberrations, malignant cell transcriptional dysregulation, and immune evasion at the cellular and tissue level, allowing us to identify targets that reprogram malignant cell states as an avenue to unleash anti-tumor immune responses.

cancer biology↗

bioRxiv2023

The colocatome as a spatial -omic reveals shared microenvironment features between tumour-stroma assembloids and lung cancer specimens

Computational frameworks to quantify and compare microenvironment spatial features of in-vitro patient-derived models and clinical specimens are needed. Here, we acquired and analysed multiplexed immunofluorescence images of human lung adenocarcinoma (LUAD) alongside tumour- stroma assembloids constructed with organoids and fibroblasts harvested from the leading edge (Tumour-Adjacent Fibroblasts;TAFs) or core (Tumour Core Fibroblasts;TCFs) of human LUAD. We introduce the concept of the "colocatome" as a spatial -omic dimension to catalogue all proximate and distant colocalisations between malignant and fibroblast subpopulations in both the assembloids and clinical specimens. The colocatome expands upon the colocalisation quotient (CLQ) through a nomalisation strategy that involves permutation analysis and thereby allows comparisons of CLQs under different conditions. Using colocatome analysis, we report that both TAFs and TCFs protected cancer cells from targeted oncogene treatment by uniquely reorganising the tumour-stroma cytoarchitecture, rather than by promoting cellular heterogeneity or selection. Moreover, we show that the assembloids colocatome recapitulates the tumour-stroma cytoarchitecture defining the tumour microenvironment of LUAD clinical samples and thereby can serve as a functional spatial readout to guide translational discoveries. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/557278v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@a79aa3org.highwire.dtl.DTLVardef@199582forg.highwire.dtl.DTLVardef@6c7fb6org.highwire.dtl.DTLVardef@134a2aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cells protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

bioinformatics↗

bioRxiv2021

Multi-Omics Analysis of Fibroblasts from the Invasive Tumor Edge Reveals that Tumor-Stroma Crosstalk Induces O-glycosylation of the CDK4-pRB Axis

The invasive leading edge represents a potential gateway for tumor invasion. We hypothesize that crosstalk between tumor and stromal cells within the tumor microenvironment (TME) results in the activation of key biological pathways depending on their location in the tumor (edge vs core). Here, we highlight phenotypic differences between Tumor-Adjacent-Fibroblasts (TAFs) from the invasive edge and Cancer-Associated Fibroblasts (CAFs) from the tumor core, established from human lung adenocarcinomas. We use an innovative multi-omics approach that includes genomics, proteomics and, O-glycoproteomics to characterize crosstalk between TAFs and cancer cells. Our analysis shows that O-glycosylation, an essential post-translational modification resulting from sugar metabolism, alters key biological pathways including the CDK4-pRB axis in the stroma, and indirectly modulates pro-invasive features of cancer cells. In summary, aside from improving the efficacy of CDK4 inhibitors anti-cancer agents, the O-glycoproteome poses a new consideration for important biological processes involved in tumor-stroma crosstalk.

cancer biology↗

bioRxiv2021

Reconstructing co-dependent cellular crosstalk in lung adenocarcinoma using REMI

Cellular crosstalk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCI) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA-seq dataset, then leverage single-cell transcriptomics data to increase its resolution and identify LR prognostic signatures. We experimentally confirmed colocalization of CTGF:LRP6 as an interaction predicted to be associated with LUAD progression. Our work presents a novel way to reconstruct interactomes and a new approach to identify clinically-relevant cell-cell interactions.

bioinformatics↗

bioRxiv2019

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge, with relevance towards understanding biological changes ranging from cellular differentiation to epigenetic (adaptive) responses of diseased cells to drugging. We report on a combined experimental and theoretic method for determining the trajectories that specific highly plastic BRAFV600E mutant patient-derived melanoma cancer cells take between drug-naive and drug-tolerant states. Recent studies have implicated non-genetic, fast-acting resistance mechanisms are activated in these cells following BRAF inhibition. While single-cell highly multiplex omics tools can yield snapshots of the cell state space landscape sampled at any given time point, individual cell trajectories must be inferred from a kinetic series of snapshots, and that inference can be confounded by stochastic cell state switching. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naive and drug-tolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naive cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies.

bioengineering↗

bioRxiv2019

Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGF{beta}-treatment and identify through TGF{beta}-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.

systems biology↗