Biology subjects

Kok, M.

Publications and source records attributed to Kok, M..

6 recordsLinked to original sources

bioRxiv2026

Quantifying the spatio-temporal image degradation under motion blur in fluorescence microscopy

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by the specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during the full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with spectral signal-to-noise ratio (SSNR). We developed a simulation framework which models the image formation of objects undergoing arbitrary motion, to evaluate the degradation of the spatial resolution under translational and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the spectral signal-to-noise ratio degrading by up to 50% and the resolution by up to 40% for a 90{degrees} change in the motion direction. Furthermore, we show that for rotational motion, conventional radially averaged metrics such as the Fourier Ring Correlation are not able to quantify the effects of angular blur. On the other hand, the SSNR is able to accurately quantify this degradation. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

biophysics↗

bioRxiv2025

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

cancer biology↗

bioRxiv2024

Automated classification of cellular expression in multiplexed imaging data with Nimbus

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

bioinformatics↗

bioRxiv2024

TCR-engineered T-cells directed against Ropporin-1 constitute a safe and effective treatment for triple-negative breast cancer in near-clinical models

Triple-negative breast cancer (TNBC) shows an urgent need for new therapies. We discovered Ropporin-1 (ROPN1) as a target to treat TNBC with T-cells. ROPN1 showed high and homogenous expression in 90% of primary and metastatic TNBC but not in healthy tissues. HLA-A2-binding peptides were detected via immunopeptidomics and predictions and used to retrieve T-cell receptors (TCRs) from naive repertoires. Following gene introduction into T-cells and stringent selection, we retrieved a highly specific TCR directed against the epitope FLYTYIAKV that did not recognize non-cognate epitopes from alternative source proteins. Notably, this TCR mediated killing of three-dimensional tumoroids in vitro and tumor cells in vivo and outperformed standard-of-care drugs. Finally, the T-cell product expressing this TCR and manufactured using a clinical protocol fulfilled standard safety and efficacy assays. Collectively, we have identified and preclinically validated ROPN1 as a target and anti-ROPN1 TCR T-cells as a treatment for the vast majority of TNBC patients.

immunology↗

bioRxiv2021

EB3-informed dynamics of the microtubule stabilizing cap during stalled growth

Microtubule stability is known to be governed by a stabilizing GTP/GDP-Pi cap, but the exact relation between growth velocity, GTP hydrolysis and catastrophes remains unclear. We investigate the dynamics of the stabilizing cap through in vitro reconstitution of microtubule dynamics in contact with micro-fabricated barriers, using the plus-end binding protein GFP-EB3 as a marker for the nucleotide state of the tip. The interaction of growing microtubules with steric objects is known to slow down microtubule growth and accelerate catastrophes. We show that the lifetime distributions of stalled microtubules, as well as the corresponding lifetime distributions of freely growing microtubules, can be fully described with a simple phenomenological 1D model based on noisy microtubule growth and a single EB3-dependent hydrolysis rate. This same model is furthermore capable of explaining both the previously reported mild catastrophe dependence on microtubule growth rates and the catastrophe statistics during tubulin washout experiments.

cell biology↗

bioRxiv2020

Assessing ambitious nature conservation strategies within a 2 degree warmer and food-secure world

Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the CBD post-2020 global biodiversity framework. We designed two contrasting, ambitious global conservation strategies, Half Earth (HE) and Sharing the Planet (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide ecosystem services while also mitigating climate change and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the Middle of the Road Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that both conservation strategies result in a reduction in the loss of biodiversity and ecosystem services globally, but without additional measures to achieve effective climate mitigation they will be insufficient to restore biodiversity. The HE strategy performs better for terrestrial biodiversity protection (biodiversity intactness (MSA), Red List Index, geometric mean abundance) in currently still natural regions, reflecting global conservation priorities. The SP strategy yields more improvements for biodiversity in human-used areas, aquatic biodiversity and for regulating ecosystem services (pest control, pollination, erosion control), reflecting regional priorities. However, conservation only scenarios show a considerable increase in food security risks (especially in Sub-Saharan Africa) compared to the baseline and limited reduction of global temperature increase. Only when conservation strategies are combined with climate change mitigation efforts and additional actions especially in the agricultural and energy system into a portfolio of integrated sustainability measures, both conservation strategies result in restoring biodiversity to current values or even some improvement, while keeping global warming below two degrees and keeping food security risks below baseline. Minimizing food wastes and reducing consumption of animal products will be crucial.

ecology↗