Biology subjects

Heiser, L.

Publications and source records attributed to Heiser, L..

10 recordsLinked to original sources

bioRxiv2026

An in vivo platform to jointly monitor cellular and metabolic responses to chemotherapy.

How drug treatments reshape immune and metabolic states within intact tumors remains difficult to study with existing methods. We introduce a spatial pharmacology platform that enables parallel analysis of multiple agents within a single tumor, linking local drug exposure to immune and metabolic remodeling. Using a microdevice for localized drug delivery, we created a large-scale paired CyCIF-MALDI dataset spanning 1.5 million cells across 27 MMTV-PyMT tumor sections and nine treatment programs, enabling integrated spatial pharmacology at unprecedented scale. Metabolic signatures robustly predict proteomic spatial neighborhoods establishing metabolism as a powerful predictor of tumor organization and immune phenotype. Within this framework, we identify a dominant metabolic axis defined by the myeloid polarization between CSF1R+ tumor-associated macrophages and MPO+ infiltrating myeloid cells localized near regions of drug-induced tumor cell death. Finally, we detect putative lipid-associated macrophage (LAM)-like populations within drug-resistant treatment regions.

bioengineering↗

bioRxiv2025

Automated Quality Control of Time-Course Imaging from 3D in vitro cultures

Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. Existing computational methods struggle to address this issue. We present a new algorithm to evaluate data integrity and rectify mislabeling in longitudinal imaging of 3D cell culture. Our algorithm integrates permutation-based optimization with Procrustes analysis. By using X and Y coordinates of images, it accurately reorders, matches, and aligns object positions across time points, correcting for rotation, translation, and small movements. Validation with simulated data confirmed its accuracy and robustness. Applied to longitudinal imaging of tumor spheroids, our algorithm revealed frequent displacement amongst the spheroids between time points and corrected many mislabeled images. This computationally efficient and adaptable method needs no experimental adjustments and presents a readily accessible solution for data quality control. MotivationThree-dimensional (3D) in vitro models, such as tumor organoids and spheroids embedded in an extracellular matrix, are increasingly vital for studying normal and disease biology, including drug responses.1-3 A key advantage of these models is that imaging platforms can perform continuous longitudinal imaging to track phenotypic changes. However, common issues in 3D techniques, such as matrix shifts during experimental setup or image capture, can introduce technical artifacts that affect downstream analyses. Currently, no automated analytical approaches exist for assessing or correcting technical artifacts. Here, we introduce a robust, automated algorithm for assessing the quality of time-course image data and, in some cases, correcting object mislabeling to enable accurate tracking of individual spheroids over time. This approach relies only on image metadata, requiring no experimental modifications. It offers a readily implementable solution for improving data integrity and reproducibility and enhancing the reliability of longitudinal 3D cell culture studies.

bioengineering↗

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↗

bioRxiv2024

Cell identity revealed by precise cell cycle state mapping links data modalities

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

bioinformatics↗

bioRxiv2022

A Novel Mouse Model that Recapitulates the Heterogeneity of Human Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new targeted therapies, but few TNBC mouse models exist. Here, we developed a novel TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl murine model develops TN mammary tumors that display histological and molecular features commonly found in human TNBC. We performed deep omic analyses on Myc;Ptenfl tumors including machine learning for morphologic features, bulk and single-cell RNA-sequencing, multiplex immunohistochemistry and single-cell phenotyping. Through comparison with human TNBC, we demonstrated that this new genetic mouse model develops mammary tumors with differential survival that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC; providing a unique pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response. Statement of significanceThe development of cancer models that mimic triple-negative breast cancer (TNBC) microenvironment complexities is critical to develop effective drugs and enhance disease understanding. This study addresses a critical need in the field by identifying a murine model that faithfully mimics human TNBC heterogeneity and establishing a foundation for translating preclinical findings into effective human clinical trials.

cancer biology↗

bioRxiv2022

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) for identification of context-specific networks and new regulatory mechanisms

Cell phenotypes are dictated by both extra- and intra-cellular contexts, and robust identification of context-specific network features that control phenotypes remains challenging. Here, we developed a multi-omics data integration strategy called MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with specific cellular phenotypes. We applied this method to chromatin accessibility, mRNA, protein, and phospho-protein time course datasets and focus on two illustrative use cases after we show MOBILE could recover known biology. First, MOBILE nominated new mechanisms of interferon-{gamma} (IFN{gamma}) regulated PD-L1 expression, where analyses suggested, and literature supported that IFN{gamma}-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes. Second, we explored differences between the highly similar transforming growth factor-beta 1 (TGF{beta}1) and bone morphogenetic protein 2 (BMP2) and showed that differential cell size and clustering properties induced by TGF{beta}1, but not BMP2, were related to the laminin/collagen pathway activity. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly applicable to identify context-specific molecular features associated with cellular phenotypes. Graphical Summary O_FIG O_LINKSMALLFIG WIDTH=162 HEIGHT=200 SRC="FIGDIR/small/501297v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@12fa3a5org.highwire.dtl.DTLVardef@a0b6a8org.highwire.dtl.DTLVardef@137efb7org.highwire.dtl.DTLVardef@15aec85_HPS_FORMAT_FIGEXP M_FIG C_FIG Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) pipeline yields statistically robust, context-specific association networksThe MOBILE pipeline integrates omics datasets in a data-driven, biologically-structured manner. The pipeline outputs are gene-level, contextspecific association networks. These association networks nominate differentially enriched pathways, subnetworks, and new connections. Broadly applicable to find condition specific networks using multi-omics datasets.

systems biology↗

bioRxiv2021

ME-VAE: Multi-Encoder Variational AutoEncoder for Controlling Multiple Transformational Features in Single Cell Image Analysis

Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenged by the lack of robust methods to segment cells, identify subcellular compartments, and extract relevant features. Variational autoencoder (VAE) approaches produce encouraging results by mapping an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to uninformative technical variation. Herein, we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features, including emergent features not obvious from prior knowledge. We show that the proposed architecture improves analysis by making distinct cell populations more separable compared to traditional VAEs and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other analytic modalities.

bioinformatics↗

bioRxiv2020

Single cell tracking based on Voronoi partition via stable matching

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

systems biology↗

bioRxiv2020

Invasion of homogeneous and polyploid populations in nutrient-limiting environments

Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Progression is accompanied by a switch to an invasive cell phenotype. Nutrient-limiting environments promote chemotaxis with aggressive morphologies characteristic of invasion. It is unknown how co-existing cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. We integrate mathematical modeling with microenvironmental perturbation-data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Hereby, subpopulations are defined by their energy efficiency and chemotactic ability. We estimate the invasion-distance traveled by a homogeneous population. For heterogeneous populations, our results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will spatially segregate the two populations and only one type will dominate at the invasion front. Only if these two phenotypes are balanced do the two subpopulations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity to discern circumstances when inhibiting chemotaxis amplifies internal competition and decelerates tumor progression, from circumstances that render clinical consequences of chemotactic inhibition unfavorable.Significance A better understanding of the nature of the double-edged sword of high ploidy is a prerequisite to personalize combination-therapies with cytotoxic drugs and inhibitors of signal transduction pathways such as MTOR-Is.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2017

Force-dependent ligand sensing enables αVβ3 integrin to differentiate between fibronectin and high-affinity ligands

V{beta}3 integrin recognizes multiple extracellular matrix proteins, including vitronectin (Vn) and fibronectin (Fn). However, cell experiments are frequently performed on homogenously coated substrates with only one integrin ligand present. Here, we employed binary-choice substrates of Fn and Vn to dissect V{beta}3 integrin-mediated binding to both ligands on the subcellular scale. Superresolution imaging revealed that V{beta}3 integrin preferred binding to Vn under various conditions. In contrast, binding to Fn required mechanical load on V{beta}3 integrin. Integrin mutations, structural analysis, and molecular dynamics simulations established a model where the extended-closed conformation of V{beta}3 integrin binds Vn but not Fn. Force-mediated hybrid domain swing-out characterizes the extended-open conformation needed for efficient Fn binding. Thus, force-dependent conformational changes in V{beta}3 integrin increase the number of available ligands and therefore the ligand promiscuity of this integrin. These findings for V{beta}3 integrin were shown to regulate cell migration and mechanotransduction differentially on Fn compared to Vn and therefore to regulate cell behavior.

cell biology↗