Biology subjects

Jayaprakash, C.

Publications and source records attributed to Jayaprakash, C..

8 recordsLinked to original sources

bioRxiv2025

Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers

Solid tumors are characterized by a spatially heterogeneous mixture of cancer cells, immune cells and other non-tumor cells. Recent characterization of the heterogeneity at the single cell level has revealed spatial patterns of different cell types often lacking a simple geometric structure associated with cancer progression. Here we investigated the occurrence of physical fencing of tumor cells by specific immune and non-immune phenotypes in the tumor microenvironment (TME) and the association of these clusters to cancer progression in a wide range solid tumors formed in different organs. We analyzed published patient response and imaging mass cytometry (IMC) datasets from tumor microarrays obtained from tumor tissues in triple-negative breast cancer (279 patients), lung cancer (416 patients), melanoma (30 patients), colorectal cancer (9 patients), glioma (185 patients), and head and neck cancer (139 patients) to characterize the presence of fencing clusters of various cell types and their association with differing patient outcomes. Devising and employing simple mechanistic and stochastic spatially-resolved computational models we quantify the dependence of the pro- and anti-tumor roles of a fencing cluster on the size and the lifetime of the cluster, as well as the chemokine gradient in the local environment. We unveiled that spatial patterns of immune cells, especially through fencing tumor boundary, affects tumor progression and treatment responsiveness to immunotherapy.

cancer biology↗

bioRxiv2025

Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

Brain cancer is one of the most aggressive forms of cancer in the central nervous system occurring as primary or metastatic tumors. Sequencing of resected tissues from glioblastoma (GBM) and brain metastases (BrMET) reveals high heterogeneity in neoantigens and T cell receptor (TCR) repertoires. Our analysis of published sequencing data in different spatial regions of tumors GBM and BrMET patients show the presence of T cell clones of sizes with a heavy right-tailed distribution spanning several orders of magnitude (e.g., 1 - 1000 cells) with a few (<10) large clone sizes and many small clones. We investigated how neoantigens in the tumor microenvironment (TME) drive T cell expansion in GBM and BrMET by developing a mechanistic mathematical model based on the interaction of T cells and the neoantigens that incorporates their stochastic proliferation in the immunosuppressive environment and trained it to predict the emergence of T cell clones in different spatial regions. The model accurately predicts the distribution of observed T cell clone sizes and reveals that the strength of interaction between TCR and neoantigen-MHC complex and stochastic T cell proliferation crucially regulates T cell expansion in the TME. It also suggests higher rate of T cell proliferation BrMET compared to GBM. An extended version of the model predicts the ability of individual neoantigens to generate T cell clones in the periphery in patients receiving personalized neoantigen vaccines. Our model may facilitate the discovery of improved peptide combinations in neoantigen vaccine studies. Significance StatementNeoantigen-driven T cell responses are key to immune defense against solid tumors. Multi-region sequencing of brain tumors reveals spatial heterogeneity in neoantigens and T cell repertoires. To understand whether neoantigen-driven T cell expansion underlies the TCR repertoire heterogeneities, we developed a stochastic, mechanistic model of T cell proliferation using published TCR and neoantigen data from primary and metastatic brain tumors. The model accurately predicts clone size distributions, showing faster T cell proliferation in metastases and stronger responses to clonal (shared) neoantigens than to private (region-specific) ones. The model is extended to describe T cell clonal expansion in the periphery in response to neoantigen vaccine in glioblastoma patients. This framework may help design optimal peptide combinations in neoantigen vaccine development.

cancer biology↗

bioRxiv2025

Spatial statistics of submicron size clusters of activating and inhibitory Natural Killer cell receptors in the resting state regulate early time signal discrimination

Natural Killer (NK) cells are lymphocytes of the innate immunity and sense healthy or diseased target cells with activating and inhibitory NK cell receptor (NKR) molecules expressed on the cell surface. The protection provided by NK cells against viral infections and tumors critically depends on their ability to distinguish healthy cells from diseased target cells that express 100- fold more activating ligands. NK cell signaling and activation depend on integrating opposing signals initiated by activating and inhibitory NKRs interacting with the cognate ligands expressed on target cells. A wide range of imaging experiments have demonstrated aggregation of both activating and inhibitory NKRs in the plasma membrane on submicron scales in resting NK cells. How do these submicron size NKR clusters formed in the resting state affect signal discrimination? Using in silico mechanistic signaling modeling with information theory and published superresolution imaging data for two well-studied human NKRs, activating NKG2D and inhibitory KIR2DL1, we show that early time signal discrimination by NK cells depends on the spatial statistics of these clusters. When NKG2D and KIR2DL1 clusters are disjoint in the resting state, these clusters help NK cells to discriminate between target cells expressing low and high doses of the activating cognate ligand, whereas, when the NKR clusters fully overlap the NK cells are unable to distinguish between healthy and diseased target cells. Therefore, the spatial statistics of submicron scale clusters of activating and inhibitory NKRs at the resting state provides an additional layer of control for signal discrimination in NK cells. SignificanceSignal integration of opposing signals initiated by activating and inhibitory NK cell receptors (NKRs) in a noisy environment determine an NK cells response to healthy and diseased target cells. Superresolution microscopy imaging revealed aggregation of NKRs in submicron scales in resting NK cells. Using computational modeling, information theory, and published imaging data, we show when these clusters of the opposing NKRs are disjoint, the NK cells can separate healthy from diseased target cells but fail to do so when the clusters overlap. Thus, spatial statistics of submicron-sized NKR clusters in the resting state provide a lever for distinguishing self from non-self. The results suggest spatial organization of receptors in the resting state in may modulate signal discrimination in immune cells.

immunology↗

bioRxiv2025

An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data

Recent single-cell experiments that measure copy numbers of over 40 proteins in individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the time-evolution of protein abundances that could yield mechanisms that underlie signaling kinetics. We recently developed a generalized method of moments (GMM) based approach that estimates parameters of mechanistic models using TSS data. However, when multiple mechanistic models potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. Popular approaches like Kullback-Leibler divergence and Akaikes Information Criterion are difficult to implement because the distribution that gave rise to the "noisy" data is only known numerically and approximately. To perform model selection in this situation, we introduce an entropy-based approach that incorporates our GMM based parameter estimation and commonly used estimators in kernel density estimation. Using simulated TSS data, we show that our approach can select the "ground truth" from a set of competing mechanistic models. Furthermore, we use a bootstrap procedure to compute model selection probabilities, which can be useful when measuring the relative support of a candidate model.

biophysics↗

bioRxiv2023

Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immune checkpoint inhibitors in patients with melanoma

High-dimensional, spatial single-cell technologies such as CyTOF imaging mass cytometry (IMC) provide detailed information regarding locations of a large variety of cancer and immune cells in microscopic scales in tumor microarray (TMA) slides obtained from patients prior to immune checkpoint inhibitor (ICI) therapy. An important question is how the initial spatial organization of these cells in the tumor microenvironment (TME) change with time, regulate tumor growth and eventually outcomes as patients undergo ICI therapy. Utilizing IMC data of melanomas of patients who later underwent ICI therapy, we develop a spatially resolved interacting cell systems model that is calibrated against patient response data to address the above question. We find that the tumor fate in these patients is determined by the spatial organization of activated CD8+ T cells, macrophages, and melanoma cells and the interplay between these cells that regulate exhaustion of CD8+ T cells. We find that fencing of tumor cell boundaries by exhausted CD8+T cells is dynamically generated from the initial conditions that can play a pro-tumor role. Furthermore, we find that specific spatial features such as co-clustering of activated CD8+ T cells and macrophages in the pre-treatment samples determine the fate of the tumor progression, despite stochastic fluctuations and changes over the treatment course. Our framework enables determination of mechanisms of interplay between a key subset of tumor and immune cells in the TME that regulate clinical response to ICIs. SignificanceRecent advances in single cell technologies allows for spatial imaging a wide variety of cancer and immune cells in tissue samples obtained from solid tumors. This detailed snapshot data of microscale organization of tumor and immune cells could provide valuable insights into underlying biology and clinical responsiveness to cancer immunotherapy. By combining published data from imaging mass-cytometry and patient response against ICI drugs with data analysis rooted in statistical physics and statistical inference theory, we developed and studied the dynamics of mechanistic spatially resolved models: we show that tumor growth during ICI treatment is regulated by non-intuitive interplay between CD8+ T cells and tumor associated macrophages, formation of a pro-tumor fencing of exhausted CD8+ T cells around melanoma cells, specific features of spatial organization of these cells prior to treatment, and stochastic fluctuations in the dynamics. The mechanisms unveiled in our studies are general and can pertain to the response of other solid tumors to ICI therapy.

immunology↗

bioRxiv2023

H2A.Z histone variants facilitate HDACi-dependent removal of H3.3K27M mutant protein in paediatric high-grade glioma cells

Diffuse intrinsic pontine gliomas (DIPG) are deadly paediatric brain tumours, non-resectable due to brainstem localisation and diffusive growth. Patients with DIPG have a dismal prognosis of 9-12 months of survival with no effective therapy. Over 80% of DIPGs harbour a mutation in histone 3 (H3.3 or H3.1) resulting in a lysine to methionine substitution (H3K27M). H3K27M causes global epigenetic alterations (a loss of H3K27 trimethylation and an increase in H3K27 acetylation) resulting in aberrant gene expression. To date, no therapeutic strategy exists to suppress the levels of oncogenic H3K27M. We show that pan-HDAC inhibitors (HDACi) lead to the temporary but significant reduction in the H3.3K27M protein (up to 80%) in multiple glioma cell lines expressing the H3.3K27M histone variant, without changes in the H3F3A mRNA expression. The H3.3K27M occupancy at the chromatin is greatly reduced upon HDACi (SB939) treatment, as shown by ChIPseq analysis. H3.3K27M loss is most striking at SB939-upregulated genes suggesting the role in repression of these genes. In addition, genes previously reported as H3K27M-dependent become downregulated in response to SB939 treatment. We discover that the SB939-mediated loss of H3.3K27M is partially blocked by a lysosomal inhibitor, chloroquine. Moreover, the loss of H3.3K27M is facilitated by co-occurrence of H2A.Z, as evidenced by the knock-down of H2A.Z histone isoforms. ChIPseq analysis confirms the occupancy of H3.3K27M and H2A.Z at the same SB939-inducible genes. Altogether, we provide new insight into disease-specific mechanism of HDAC inhibition and demonstrate pharmacological modulation of the oncogenic H3.3K27M protein levels. These findings open a new possibility to directly target the H3.3K27M oncohistone, which may be exploited in future therapies.

cancer biology↗

bioRxiv2022

BioNetGMMFit: a Parameter Estimation Tool for BioNetGen using Single-Cell Snapshot Data from Cell Populations Evolving over Time

BackgroundMechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. ResultsBioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, timestamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetG-MMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. ConclusionBy streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

systems biology↗

bioRxiv2022

Generalized Method of Moments improves parameter estimation in biochemical signaling models of time-stamped single-cell snapshot data

MotivationOrdinary differential equations are commonly used to model the sub-cellular dynamics of average values of proteins and mRNAs. New single-cell technologies provide cell-to-cell differences in protein/mRNA abundances that allow for the evaluation of higher order moments. However, using this additional information to improve parameter estimation can be challenging since the magnitudes of single-cell abundances can vary widely between proteins/mRNA. ResultsWe employ Generalized Method of Moments (GMM) and Particle Swarm Optimization to address the above challenges in mechanistic modeling of signaling kinetics data. Using synthetic data from linear and non-linear models, we show that the proposed method improves parameter estimation and enables construction of approximate confidence intervals. Furthermore, our approach exploits parallel computation to scale with increasing data size and dimensions. We apply our software CyGMM to estimate parameters in a linear ODE model for publicly available longitudinal single-cell cytometry data for CD8+ T cells. Our results demonstrate substantial improvements for modeling data from single-cell cytometry and RNA-seq experiments. AvailabilityWe also make freely available our estimation software CyGMM written in C++ on github (https://github.com/jhnwu3/CyGMM). Contactjayajit@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗