Biology subjects

Copperman, J.

Publications and source records attributed to Copperman, J..

8 recordsLinked to original sources

bioRxiv2026

Spatially coordinated RTK-ERK signaling dynamics shape osteosarcoma single-cell drug response in the lung microenvironment

Purpose: Lung metastases are the primary cause of death in osteosarcoma patients and treatment response often vary due to convergence of genetic and phenotypic influences in the context of a dynamic tumor-host tissue environment. The purpose of this study was to determine how dynamic tumor- and host-driven receptor tyrosine kinase (RTK)-ERK signaling interactions contribute to phenotypic drug response heterogeneity. Experimental Design: Live-cell time-lapse microscopy and data-driven dynamical modeling was used to resolve ERK activity, FOSL1/Fra-1 reporter expression, and spatiotemporal cell fate in living, biosensor-expressing osteosarcoma lung metastases ex vivo following MCL1 inhibitor exposure. RTK inhibitors were then evaluated for their ability to suppress adaptive signaling and enhance MCL1 inhibitor efficacy ex vivo and in vivo. Results: MCL1 inhibition induced extensive tumor-cell death; however, persistent ERKhigh/Fra-1high reporter cells emerged and were enriched at the tumor-lung interface, consistent with adaptive resistance. Experimental and computational approaches revealed that tumor-cell lysis amplified fibroblast growth factor receptor (FGFR)-mediated ERK signaling in neighboring cells, promoting a targetable drug-resistant state marked by adaptive ERK induction, FOSL1/Fra-1 expression, and correlated with upregulation of known drug resistance pathways Nrf2 and HMOX1. Combinatorial inhibition of FGFR with fexagratinib, or broad-spectrum RTK inhibitor, pazopanib, suppressed adaptive ERK activation and enhanced MCL1 inhibitor response resulting in greater efficacy ex vivo and in vivo. Conclusions: Tumor-host and tumor-cell-lysis-induced FGFR-ERK signaling contribute to spatiotemporally heterogeneous adaptive resistance in metastatic osteosarcoma. These data support combinatorial targeting of RTK signaling with cytotoxic or targeted agents as a strategy to block adaptive resistance and improve treatment efficacy.

cancer biology↗

bioRxiv2026

A Compact, Standalone & Battery-Powered 3D Organoid-on-Chip System with Programmable Flow Control

Organoids-on-chip combine the 3D complex microenvironment and cellular composition of organoids with microfluidic flow, increasing nutrient-waste exchange and mimicking the contributions of in vivo interstitial and vascular flow. However, the widespread adoption of organoid-on-chip platforms is limited by the lack of incubator-friendly flow control systems. Existing approaches often rely on commercially available syringe or peristaltic pumps, but these are bulky, lack scalability, and present a significant barrier for clinical translation. To circumvent these issues, we present the Compact Active Perfusion Standalone Organoid-on-Chip (CAPS-OC) platform, a fully integrated and battery-powered microfluidic system capable of culturing organoids in active media flow. To achieve this, we introduce a novel low-power mechanism of pressure pulse generation using an off-the-shelf compact rotary actuator (CRA), and package it into a compact electromechanical assembly. This assembly provides timed pneumatic inputs to achieve programmable control of membrane-based peristaltic pumps, with [~]100 {micro}L/hr dynamic range achieved on a custom microfluidic organoid chip. We biologically validated this system by culturing pancreatic cancer organoids derived from a KrasLSL-G12D/+; Trp53LSL-R172H/WT; Pdx1-Cre (KPC) genetically engineered mouse model. We found that our chip enhances proliferation and helps sustain a population of larger (>150 {micro}m) organoids compared to standard dome based static culture. Additionally, through immunostaining, we observe that KPC organoids cultured in the chip show more aggressive PDAC phenotype with reduced nuclear expression of GATA6, whereas organoids in static culture retain less aggressive classical- like subtype. Finally, testing of a RAS inhibitor drug, daraxonrasib, on the KPC organoids on chip showed size-based sensitivity elucidating the impact of active perfusion on the drug diffusion kinetics. Altogether, we establish CAPS-OC as a valuable tool for the bioengineering community and for clinically translating organoid model systems.

bioengineering↗

bioRxiv2025

Serial Imaging of Tumor and microEnvironment (SITE) platform for live-cell ex vivo modeling of primary and metastatic cancer dynamics.

Understanding cancer initiation and progression is extremely challenging, in part due to experimental limitations in measuring and interpreting key signalling and tumour-microenvironment (TME) interactions that determine changes in cell and tissue behaviours over time. Here we developed SITE (Serial Imaging of Tumour and microEnvironment), a spatially-and temporally integrated, modular, ex vivo platform enabling quantitative analysis of TME interaction dynamics, signalling, and cell fate at single cell and tissue scales. Applied to modelling primary and lung metastatic breast cancer, SITE revealed tissue-specific TME interactions and ERK signalling patterns linked to distinct single-cell behaviours. We found that the earliest steps in tumour establishment and metastatic seeding involved active cell protrusion and the establishment of a multicellular niche interfacing tumour and host. Experimental and mathematical modelling showed that ERK signalling was co-influenced by these interactions, where cancer cluster formation increased signalling via the establishment of local signalling circuits. Disruption of these signalling circuits led to tissue-specific impacts on cancer intrinsic and TME interaction dynamics. Here, we modelled breast cancer as a test case, demonstrating the broad utility of SITE for quantitative exploration of TME interaction dynamics- closing a significant gap in experimental capabilities between in vivo models and in vitro systems.

cancer biology↗

bioRxiv2025

Integrative Analysis of EGF, OSM, and TGFB Signaling Pathways Reveals Synergistic Mechanisms Driving Cell Motility Through CXCR2 Chemotactic Signaling and CREB Activation

The microenvironment surrounding cells plays a critical role in determining cellular phenotype. Key components of the microenvironment include the diverse milieu of ligands and cytokines bind cell surface receptors to initiate changes in molecular programs. While the responses to extracellular signals have been extensively studied in isolation, little is known about the effects of combinations of signals on phenotypic and transcriptional responses. In this study, we used a coordinated approach to systematically investigate the combinatorial effects of the cytokines Oncostatin M (OSM) and Transforming Growth Factor Beta 1 (TGFB), and the growth factor Epidermal Growth Factor (EGF) on MCF10A mammary epithelial cells. Quantitative analysis of live-cell imaging data revealed a complex array of phenotypic responses after ligand treatment, including changes in proliferation, motility, cell clustering, and cytoplasmic size. We observed that all ligand combinations produce emergent phenotypic responses distinct from the maximal effects of individual ligands, indicating induction of new molecular programs. Companion RNA sequencing studies revealed a synergistic upregulation of a small but specific transcriptional program, including genes involved in cell migration, epithelial differentiation, and chemotactic signaling. Notably, these included chemokines such as CXCL3, CXCL5, and PPBP, which are known drivers of epithelial proliferation and migration. Additionally, transcription factor enrichment analyses and Reverse Phase Protein Array (RPPA) studies highlighted distinct changes in pathway utilization and transcription factor activity following combination treatment, including enhanced activation of MAP kinase and CREB signaling, compared to treatment with either agent alone. Using partial least squares regression, we identified robust transcriptional signatures associated with quantitative cellular phenotypes. We validated these signatures in independent datasets, confirming that they generalize across cellular contexts. Finally, an in-depth functional analysis of cell motility with RNA interference and pathway inhibition revealed that synergistic upregulation of CXCR2 signaling, mediated by CREB transcription factor activation, contributes to increases in cell motility across ligand conditions. These findings underscore the importance of combinatorial signaling in reprogramming epithelial phenotypes and reveal potential therapeutic targets for disrupting synergistic pathways in disease contexts such as cancer progression. Together, this study provides a framework for understanding how complex ligand interactions shape phenotypic and molecular landscapes.

cell biology↗

bioRxiv2025

Inferring single-cell dynamics of a molecular reporter from unlabeled live-cell light microscopy data analyzed with delay embedding

Quantification of the temporal sequence of molecular behavior in live individual cells holds promise for improving causal and mechanistic models of cell biology. In recent years, different methods for inferring molecular labeling from microscopy data have been developed, especially in the context of "virtual pathology", but less effort has been directed to the context of single-cell dynamics and live-cell imaging. We demonstrate that phase-contrast live-cell imaging of MCF10A cells, without labeling data, is predictive of dynamical, single-cell behavior of the cell-cycle reporter Human DNA Helicase B (HDHB) - in particular, of the nuclear vs. cytoplasmic localization of this fluorescent reporter of cyclin-dependent kinase activity. Prediction quality improves substantially when temporal sequences of images are combined in a "delay embedding" framework. When different featurizations of the imaging data are examined, we find that features derived from a variational auto-encoder (VAE) outperform "classical" image features derived from shape and texture. We find the best performance, with Pearson R[~] 0.9 on test data, using VAE features augmented by categorical predictions, all within the delay-embedding framework - in comparison to R [~] 0.5 based on "ordinary" regression with VAE features.

cell biology↗

bioRxiv2024

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals change the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and predicts expression of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data. SummaryIn normal homeostatic tissues, extracellular signals induce changes in the behavior and state of epithelial cells, and aberrant responses to such signals are associated with diseases. To decode and potentially steer these responses, it is essential to link live-cell behavior to molecular programs; however, high-throughput molecular techniques are destructive or require fixation. Here we present a novel computational approach to connect single-cell measures of cell phenotype and behavior to bulk molecular readouts, enabling prediction of dynamic changes in gene expression programs. This reveals molecular programs associated with distinct cell states and identifies drivers that may be manipulated to control cell state change.

cell biology↗

bioRxiv2021

Morphodynamical cell-state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories--that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

systems biology↗

bioRxiv2020

Connexin-46/50 in a dynamic lipid environment resolved by CryoEM at 1.9 A

Gap junctions establish direct pathways for connected cells and tissues to transfer metabolic and electrical messages1. The local lipid environment is known to affect the structure, stability and intercellular channel activity of gap junctions2-5; however, the molecular basis for these effects remains unknown. To gain insight toward how gap junctions interact with their local membrane environment, we used lipid nanodisc technology to incorporate native connexin-46/50 (Cx46/50) intercellular channels into a dual lipid membrane system, closely mimicking a native cell-to-cell junction. Structural characterization of Cx46/50 lipid-embedded channels by single particle CryoEM revealed a lipid-induced stabilization to the channel, resulting in a 3D reconstruction at 1.9 [A] resolution. Together with all-atom molecular dynamics (MD) simulations and 3D heterogeneity analysis of the ensemble CryoEM data, it is shown that Cx46/50 in turn imparts long-range stabilization to the dynamic local lipid environment that is specific to the extracellular lipid leaflet of the two opposed membranes. In addition, nearly 400 water molecules are resolved in the CryoEM map, localized throughout the intercellular permeation pathway and contributing to the channel architecture. These results illustrate how the aqueous-lipid environment is integrated with the architectural stability, structure and function of gap junction communication channels, and demonstrates the ability of CryoEM to effectively characterize dynamical protein-lipid interactions.

biophysics↗