Biology subjects

Scrudders, K. L.

Publications and source records attributed to Scrudders, K. L..

5 recordsLinked to original sources

bioRxiv2025

CAR T cell cytotoxic responses are rapidly generated and sensitive to the unligated TCR

Chimeric antigen receptor (CAR) T cells expressing tumor-targeting engineered receptors can robustly eliminate cancer cells through secretion of cytotoxic factors. Durable remission in leukemia and lymphoma treatment has not been matched in solid tumors. Efforts to maximize tumor destruction and minimize toxicities have driven efforts to tune CAR signaling. However, the molecular mechanisms for CAR triggering and thresholds for activation are incompletely understood. Here, we measured the collection of CAR binding interactions that culminate in polarized delivery of lytic granules to the junction with the target. CAR T cells binarized cytotoxic activities in response to a few binding events and population outcomes were dominated by a subset of cells. Activation at the single molecule level matches the sensitivity of the native T cell receptor (TCR) and points to potent downstream signal propagation. Disruption of the unligated TCR with a transmembrane-targeting inhibitory peptide strongly dampened CAR T cell activation, indicating a critical crosstalk between the two receptors. Harnessing CAR T cell efficacy and reduction of toxicity will require new approaches to modify integration of the binding events, collected stochastically, that are rapidly digitized. These sensitive CAR T cell responses provide new insights into driving cytotoxic signaling through surface interaction engineering.

immunology↗

bioRxiv2025

A mechanistic model for dynamics between CAR T cells and target cells captures features that determine killing profiles

Chimeric antigen receptor (CAR) T cells represent a potent, programmable therapeutic that repurposes T cell cytotoxicity toward target cell elimination. Direct killing of tumor cells has been demonstrated in several cancer contexts but deficits in destroying solid tumors have been attributed to molecular and mechanical complexity. Understanding and predicting the efficacy of CAR T cell therapy requires a rigorous framework to capture the mechanistic interactions between immune cells and tumor targets. Toward enumerating such features and to quantitatively describe tumor cell survival in response to CAR T cell visitation, we propose a parametric hazard rate model for the right-censored conditional lifetime of a tumor cell, having the form of a time-discounted integral of cell:cell engagement. The model, conditioned using data on the numbers, durations, and modes of contacts made during the stochastic encounters between the two cell types, extracts mechanistic details from the experiments. We suggest two types of parameters to encapsulate features of CAR T cell killing potency ({kappa}) and the resilience of the tumor ({gamma}), respectively. These parsimonious parameters, nevertheless, generate substantial insights. Firstly, phenotypic heterogeneity from interactions mediated by substrate-engaged CAR T cells dominate killing outcomes, while those initiated in suspension contribute minimally. This emphasizes that a subset of the CAR T cell population is dominating the population outcome. Secondly, the model can be used to predict the killing as a function of time upon perturbation of the system. In this case, biasing the arrival process, toward more engagement in the adherent mode, can tune the rate of killing. Collectively, insights from the model framework imply that CAR T cell accumulation and killing efficiency depend not on the sum of individual T cell interactions, but rather on the weighted, time-dependent sum of heterogeneous interactions that are environmentally modulated. The model facilitates predictive insights into how contact sequences modulate CAR T cell responses and may provide strategies to alter therapeutic regimens.

biophysics↗

bioRxiv2025

Simultaneous particle tracking, phase retrieval and point spread function reconstruction

3D tracking and localization of particles, typically fluorescently labeled biomolecules, provides a direct means of monitoring cellular transport and communication. However, sample-induced wavefront distortions of emitted fluorescent light as it passes through the sample and onto the detector often yield point spread function (PSF) aberrations, presenting an important challenge to 3D particle tracking using pre-calibrated PSFs. PSF calibration is typically performed outside cellular samples, ignoring sample-induced aberrations, which can result in localization errors on the order of tens to hundreds of nanometers, ultimately compromising sub-diffraction limited tracking. In practice, correcting sample-induced aberrations currently requires sample-specific hardware adjustments, such as adaptive optics. Yet, information on sample-induced aberrations and PSF shape can be directly decoded from data collected using a 3D imaging setup (e.g., bi-focal). To this end, we propose a framework for simultaneous particle tracking, phase retrieval, and PSF reconstruction (SPT-PR) directly from the input data themselves. We apply it to sub-diffraction tracking of lytic granules released at the immunological synapse of T cells revealing slower motions in proximity of the plasma cell membrane, consistent with assembly of the fusion machinery and, ultimately, degranulation and release of toxic payloads. To accomplish this, we operate within a Bayesian paradigm, placing continuous priors on all possible pupil phase and amplitudes warranted by the data without limiting ourselves to a finite Zernike set-thereby allowing capture of intricate pupil phase details. We benchmark our framework using a wide range of synthetic and experimental data from static to diffusing particles, and generalize to multiple diffusing particles with overlapping PSFs. Further, as a result of simultaneous particle tracking, phase retrieval, and PSF reconstruction, we retrieve the pupil phase with errors smaller than 10% under a range of realistic scenarios while demonstrating that for tracking lytic granules under an idealized Gaussian PSF assumption, we recover discrepancies as large as hundreds of nanometers.

biophysics↗

bioRxiv2025

SPTnet: a deep learning framework for end-to-end single-particle tracking and motion dynamics analysis

Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and motion parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a Transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy videos with a precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.

biophysics↗

bioRxiv2024

Direct measurement of PIP2 densities in biological membranes using a peptide-based sensor

Organization and composition of the plasma membrane are important modulators of many cellular programs. Phosphatidylinositol phosphate (PIP) lipids are low abundance membrane constituents with different arrangements of phosphate groups around an inositol head group that regulate a large number of signaling pathways. Many strategies have been developed to detect and track PIP species to monitor their clustering, mobility, and interaction with binding partners. We implement a peptide-based ratiometric sensor for the detection of PI(4,5)P2 lipids in reconstituted membrane systems that permit absolute quantification of PI(4,5)P2 densities down to physiological levels. The sensor is membrane permeable and easily applicable to measurements in living cells. Application of calibrated sensors to cells expressing common mutations in the small GTPase, Ras, showed a reshaping of surface PI(4,5)P2 levels and distributions in a mutation-specific manner. The rapid implementation of this quantitative sensing strategy to cellular studies of cellular signaling, membrane organization and dynamics should be broadly applicable.

biochemistry↗