Biology subjects

Hollenberg, M.

Publications and source records attributed to Hollenberg, M..

5 recordsLinked to original sources

bioRxiv2025

SimSpace: a comprehensive in-silico spatial omics data simulation framework

Spatial omics technologies provide rich measurements of molecular states in their native tissue context, but the development and benchmarking of computational methods remain constrained by limited access to datasets with known ground truth. We present SimSpace, a flexible framework for simulating spatial omics data for method development, stress-testing, and controlled in silico experiments. SimSpace uses a hierarchical generative model that couples tissue-scale spatial organization, local cell-type interactions, and modular omics-profile generation. Its Markov random field-based spatial model enables controllable simulation of niche structure, cell-type co-localization, phenotype-dependent density, and spatially mediated molecular interactions. SimSpace supports both reference-free simulations under user-defined generative assumptions and reference-guided simulations that calibrate spatial parameters to real datasets while generating new spatial realizations. Across Xenium, MERFISH, and CODEX examples, SimSpace reproduced multiple evaluated spatial statistics, preserved key domain-level structure in reference-guided settings, and supported benchmarking of cell-type deconvolution and spatially variable gene detection methods. We further show that inferred interaction parameters can be perturbed to generate interpretable counterfactual spatial proteomics scenarios. SimSpace is implemented as an open-source Python package with reproducible workflows for rigorous benchmarking and methodological development in spatial omics.

bioinformatics↗

bioRxiv2025

Deep Mutational Scanning Reveals EGFR Mutations Conferring Resistance to the 4th-generation EGFR tyrosine kinase inhibitor BLU-945

IntroductionOsimertinib, a covalent third-generation EGFR tyrosine kinase inhibitor (TKI) is the first-line standard of care for EGFR L858R and ex19del lung adenocarcinoma; however, tumors frequently acquire resistance through second-site mutations. Fourth-generation inhibitors designed to overcome common second-site resistance liabilities are in clinical development. MethodsWe performed deep mutational scanning (DMS) of the EGFR kinase domain in the context of an EGFR L858R driver mutation by transducing Ba/F3 cells with a saturation library of [~]17,000 EGFR L858R kinase domain variants. Ba/F3 cells expressing the DMS library were exposed to either osimertinib or BLU-945 to select for escape mutations. ResultsL718X mutations were enriched across all conditions as well as mutations private to BLU-945 treated samples including K714R, K716T, L718V, T725M, K728E, K754E/N, N771S/T, T783I, Q791L/K, G863S, S895N, K929I, and M971L. In silico pairwise comparisons of resistance profiles between each single agent condition suggested that combination treatment with osimertinib and BLU-945 would effectively suppress orthogonal resistance mechanisms, apart from L718X. A secondary DMS screen with osimertinib and BLU-945 in combination exclusively enriched for L718X mutations. L718X mutations were present in two patients treated with BLU-945 at our institution. One patient with both EGFR L858R and L718Q mutations prior to treatment was noted to have early progression. A second patient with EGFR L858R, T790M, and C797S at the time of enrollment acquired an L718V mutation at progression. ConclusionsThis study underscores the utility of comprehensive resistance profiles of single compounds, which can be used to predict the emergence of clinical resistance mutations and to devise combination treatments designed to suppress clonal escape.

cancer biology↗

bioRxiv2024

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

cancer biology↗

bioRxiv2021

Stable Flow-induced Expression of KLK10 Inhibits Endothelial Inflammation and Atherosclerosis

IntroductionAtherosclerosis preferentially occurs in arterial regions exposed to disturbed blood flow (d-flow), while regions exposed to stable flow (s-flow) are protected. The proatherogenic and atheroprotective effects of d-flow and s-flow are mediated in part by the global changes in endothelial cell gene expression, which regulates endothelial dysfunction, inflammation, and atherosclerosis. Previously, we identified Kallikrein-Related Peptidase 10 (KLK10, a secreted serine protease) as a flow-sensitive gene in arterial endothelial cells, but its role in endothelial biology and atherosclerosis was unknown. Methods and ResultsHere, we show that KLK10 is upregulated under s-flow conditions and downregulated under d-flow conditions using in vivo mouse models and in vitro studies with cultured endothelial cells (ECs). Single-cell RNA sequencing (scRNAseq) and scATAC sequencing (scATACseq) study using the partial carotid ligation mouse model showed flow-regulated KLK10 expression at the epigenomic and transcription levels. Functionally, KLK10 protected against d-flow-induced inflammation and permeability dysfunction in human artery ECs (HAECs). Further, treatment of mice in vivo with rKLK10 decreased arterial endothelial inflammation in d-flow regions. Additionally, rKLK10 injection or ultrasound-mediated transfection of KLK10-expressing plasmids inhibited atherosclerosis in ApoE-/- mice. Studies using pharmacological inhibitors and siRNAs revealed that the anti-inflammatory effects of KLK10 were mediated by a Protease Activated Receptors (PAR1/2)-dependent manner. However, unexpectedly, KLK10 did not cleave the PARs. Through a proteomics study, we identified HTRA1 (High-temperature requirement A serine peptidase 1), which bound and cleaved KLK10. Further, siRNA knockdown of HTRA1 prevented KLK10s anti-inflammatory and barrier protective function in HAECs, suggesting that HTRA1 regulates KLK10 function. Moreover, KLK10 expression was significantly reduced in human coronary arteries with advanced atherosclerotic plaques compared to those with less severe plaques. ConclusionKLK10 is a flow-sensitive endothelial protein and, in collaboration with HTRA1, serves as an anti-inflammatory, barrier-protective, and anti-atherogenic factor.

cell biology↗

bioRxiv2018

TRPV1 regulates opioid analgesia during inflammation

Acute inflammation in humans or mice enhances the analgesic properties of opioids. However, the inflammatory transducers that prime opioid receptor signaling in nociceptors are unknown. We found that TRPV1-/- mice are insensitive to peripheral opioid analgesia in an inflammatory pain model. We report that TRPV1 channel activation drives a MAPK signaling pathway accompanied by the shuttling of {beta}-arrestin2 to the nucleus. This shuttling in turn prevents: {beta}-arrestin2-receptor recruitment, subsequent internalization of agonist-bound mu opioid receptor (MOR), and suppression of DAMGO-induced inhibition of N-type calcium current observed upon desensitization. Consequently, inflammation-induced activation of TRPV1 preserves opioid analgesic potency in a mouse model of opioid receptor desensitization. Overall, our work reveals a TRPV1-mediated signaling mechanism, involving {beta}-arrestin2 nuclear translocation, that underlies the peripheral opioid control of inflammatory pain. Our data single out TRPV1 channels as modulators of opioid analgesia.

neuroscience↗