Biology subjects

Mumenthaler, S. M.

Publications and source records attributed to Mumenthaler, S. M..

9 recordsLinked to original sources

bioRxiv2025

Multi-scale network topology analysis reveals metabolic reprogramming and therapeutic vulnerabilities in the tumor microenvironment

The tumor microenvironment comprises diverse cell populations that coordinate metabolic activities to sustain malignant growth, yet the systems-level organization of these interactions remains poorly understood. Here, we present an integrated framework combining single-cell transcriptomics, genome-scale metabolic modeling, and multi-scale network geometry to decode metabolic coordination in colorectal cancer. We demonstrate that FAP+ cancer-associated fibroblasts and MARCO+ tumor-associated macrophages undergo extensive reprogramming, establishing metabolic division of labor: fibroblasts specialize in amino acid and fatty acid metabolism while macrophages adopt cancer-like nucleotide biosynthesis programs. Systematic knockout analysis identified 19 tumor-selective vulnerabilities in branched-chain amino acid catabolism, with MAOB validated as a prognostic marker through patient survival analysis. To reveal architectural organization, we applied multifractal geometric characterization and Ollivier-Ricci curvature analysis for the first time to flux-weighted metabolic networks derived from context-specific genome-scale models. While conventional network metrics failed to distinguish tumor from normal phenotypes, multifractal analysis successfully separated tissue states through coordinated architectural changes across hierarchical scales. Role transition analysis revealed that 20-25% of metabolites undergo functional reorganization, with prostaglandin and bile acid derivatives emerging as critical communication hubs between stromal populations. Curvature analysis identified pathway-specific geometric remodeling in fatty acid metabolism (fibroblasts) and leukotriene metabolism (macrophages). Our findings establish that metabolic adaptation represents ecosystem-level network reorganization rather than isolated pathway changes, providing a generalizable framework for identifying therapeutic strategies targeting cooperative metabolic networks.

systems biology↗

bioRxiv2025

Enhanced fluorescence lifetime imaging microscopy denoising via principal component analysis

Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies the autofluorescence lifetime to measure cellular metabolism, therapeutic efficacy, and disease progression. These dynamic processes are intrinsically heterogeneous, increasing the complexity of the signal analysis. Often noise reduction strategies that combine thresholding and non-selective data smoothing filters are applied. These can result in error introduction and data loss. To mitigate these issues, we develop noise-corrected principal component analysis (NC-PCA). This approach isolates the signal of interest by selectively identifying and removing the noise. To validate NC-PCA, a secondary analysis of FLIM images of patient-derived colorectal cancer organoids exposed to a range of therapeutics was performed. First, we demonstrate that NC-PCA decreases the uncertainty up to 4-fold in comparison to conventional analysis with no data loss. Then, using a merged data set, we show that NC-PCA, unlike conventional methods, identifies multiple metabolic states. Thus, NC-PCA provides an enabling tool to advance FLIM analysis across fields.

bioengineering↗

bioRxiv2023

A novel thin plate spline methodology to model tissue surfaces and quantify tumor cell invasion in organ-on-chip-models

Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.

bioinformatics↗

bioRxiv2023

Integration of Patient-Derived Organoids and Organ-on-Chip Systems: Investigating Colorectal Cancer Invasion within the Mechanical and GABAergic Tumor Microenvironment

Three-dimensional (3D) in vitro models are essential in cancer research, but they often neglect physical forces. In our study, we combined patient-derived tumor organoids with a microfluidic organ-on-chip system to investigate colorectal cancer (CRC) invasion in the tumor microenvironment (TME). This allowed us to create patient-specific tumor models and assess the impact of physical forces on cancer biology. Our findings showed that the organoid-on-chip models more closely resembled patient tumors at the transcriptional level, surpassing organoids alone. Using omics methods and live-cell imaging, we observed heightened responsiveness of KRAS mutant tumors to TME mechanical forces. These tumors also utilized the {gamma}-aminobutyric acid (GABA) neurotransmitter as an energy source, increasing their invasiveness. This bioengineered model holds promise for advancing our understanding of cancer progression and improving CRC treatments. HighlightsO_LIMicrofluidic organ-on-chip system integrated with patient-derived CRC organoids C_LIO_LIPhysical forces influence invasion, particularly in KRAS mutant tumor cells C_LIO_LIGABAergic signaling contributes to increased invasion within a dynamic TME C_LIO_LIThis model explores patient heterogeneity, TME interactions, and cancer progression C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/557797v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1b181bborg.highwire.dtl.DTLVardef@bc5b6eorg.highwire.dtl.DTLVardef@16b4fcorg.highwire.dtl.DTLVardef@c43b9f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk. We apply PhenoPop to synthetically-generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples, and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response. MotivationTumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment failure, confounds current drug screening efforts aimed at informing therapy selection. In order to optimally select treatments, understanding the frequency and drug-response profile of individual subpopulations within a tumor is critical. Furthermore, quantitative profiles of tumor drug-response heterogeneity, in combination with predictive mathematical modeling of tumor dynamics, can be used to design effective temporal drug-sequencing strategies for tumor reduction. Here, we present a method that enables the deconvolution of tumor samples into individual sub-components exhibiting differential drug-response. This method relies on standard bulk drug-screen measurements and outputs the frequencies and drug-sensitivities of tumor subpopulations. This framework can also be applied more broadly to deconvolve cellular populations with heterogeneous responses to a variety of external stimuli and environmental factors.

cancer biology↗

bioRxiv2021

REPROGRAMMING CBX8-PRC1 FUNCTION WITH A POSITIVE ALLOSTERIC MODULATOR

ABSTRACTCanonical targeting of Polycomb Repressive Complex 1 (PRC1) to repress developmental genes is mediated by cell type-specific, paralogous chromobox (CBX) proteins (CBX2, 4, 6, 7 and 8). Based on their central role in silencing and their misregulation associated with human disease including cancer, CBX proteins are attractive targets for small molecule chemical probe development. Here, we have used a quantitative and target-specific cellular assay to discover a potent positive allosteric modulator (PAM) of CBX8. The PAM activity of UNC7040 antagonizes H3K27me3 binding by CBX8 while increasing interactions with nucleic acids and participation in variant PRC1. We show that treatment with UNC7040 leads to efficient PRC1 chromatin eviction, loss of silencing and reduced proliferation across different cancer cell lines. Our discovery and characterization of UNC7040 not only revealed the most cellularly potent CBX8-specific chemical probe to date, but also corroborates a mechanism of polycomb regulation by non-histone lysine methylated interaction partners.

synthetic biology↗

bioRxiv2016

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated \"public data libraries\", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

systems biology↗

bioRxiv2016

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.

systems biology↗

bioRxiv2016

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

systems biology↗