Biology subjects

Sabri, S.

Publications and source records attributed to Sabri, S..

7 recordsLinked to original sources

bioRxiv2026

CellCage™ Technology Enables High-throughput Isolation of Defined Cell Combinations to Resolve the Determinants of CAR-T mediated Cytotoxicity

Understanding the cellular determinants of CAR-T cell-mediated cytotoxicity at the single-cell level remains a critical challenge in developing effective cellular immunotherapies. Conventional bulk co-culture assays provide only population-level measurements of cytotoxic activity and cannot resolve the functional heterogeneity of individual effector-target interactions. Here, we demonstrate the Cellanome platform, which enables the formation of CellCage Enclosures (CCEs) through spatially controlled photopolymerization, as a tool for high-resolution, high-throughput characterization of CAR-T cell-mediated cytotoxicity. By isolating defined effector-target combinations within individual CCEs and coupling encapsulation with longitudinal time-lapse imaging and automated image analysis, we resolve cytotoxic activity across thousands of individually tracked CCEs. Using anti-CD19 CAR-T effectors and NALM6-GFP targets, we show that individual CD8+ CAR-T effectors exhibit substantial functional heterogeneity, with serial killing capacity, effector-target contact dynamics, and intrinsic motility each independently correlating with cytotoxic potency. Cytotoxic efficacy scaled positively with effector number, and CD4+ T helper cells augmented CD8+-mediated killing in a dose-dependent manner, providing direct evidence for cooperative T cell behavior during tumor cell clearance. Extending the platform to a solid tumor model using AIC100 anti-ICAM-1 CAR-T cells and HeLa-GFP targets, we show that individual AIC100 effectors are insufficient to mediate effective killing, while multi-effector CCEs exhibit robust cytotoxicity scaling with effector abundance and CD4+:CD8+ composition. These data establish the Cellanome platform as a broadly applicable system for dissecting the determinants of CAR-T cell cytotoxicity at single-cell resolution, with direct translational relevance to the optimization of cellular immunotherapy.

immunology↗

bioRxiv2026

Predictive Feature Engineering for Stress Detection using Physiological Signals, A Comparative Study

This paper presents a two-stage pipeline for implicit feature engineering in time series-based physiological stress detection using electrodermal activity (EDA) signals. In the first stage, we forecast three descriptive statistics of future EDA signals over short horizons (3, 5, and 10 seconds) based on a 60-second context window. In the second stage, a lightweight linear classifier detects stress from these predicted statistics. We evaluate three forecasting architectures spanning the domain expertise spectrum: a domain-specific bidirectional long short-term memory (BiLSTM) recurrent neural network, zero-shot and fine-tuned variants of Amazon Chronos T5 time series foundation model, and the Tabular Prior-data Fitted Network (TabPFN) applied to engineered physiological features. Experiments on the publicly available Wearable Stress and Affect Detection (WESAD) dataset, comprising chest-worn multimodal physiological signals from 15 subjects under baseline and stress conditions, use subject-independent 5-fold cross-validation and show that the domain-specific BiLSTM and TabPFN achieve comparable classification performance, with mean area under the receiver operating characteristic curve (AUC) values of 0.859-0.882 and 0.863-0.883 respectively. Both remain well ahead of the Chronos variants, which yield 0.629-0.777. Chronos models quickly reach performance saturation regardless of training depth, highlighting challenges in tokenizing continuous physiological time series. The proposed approach advances implicit feature engineering for wearable stress monitoring by leveraging forecasting as a powerful inductive bias, thereby improving robustness and providing insights into the limitations of the foundation model for physiological signals.

bioengineering↗

bioRxiv2026

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.

systems biology↗

bioRxiv2026

Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging

Single-cell technologies, encompassing molecular, morphological, and functional assays, have emerged as cornerstones of modern biological research and discovery. However, current experimental methods often fail to explicitly link these omic modalities, especially in live cells or longitudinally through time, impeding the study of multi-scale interactions and mechanisms of regulation. CellCage Enclosure (CCE) technology overcomes these limitations by dynamically compartmentalizing cells, allowing for scalable, live-cell, longitudinal exploration and simultaneous analysis of transcriptomic, proteomic, and morphological profiles. Using this novel technology, we generate previously inaccessible insights across various in vitro cellular systems under a diverse set of perturbations, including the discovery of morphological and proteomic features linked to immune suppressive gene set expression in human primary regulatory T cells (Tregs), as well as direct association of morphological and proteomic features with inflammatory gene modules in human colonic fibroblasts. We then develop a novel pooled CRISPR genetic screening technology using CCEs, PERTURB-LINK (PERTURBational LINKage of transcriptomics and imaging in single cells via enclosure-based screening) and apply this approach in murine bone marrow derived macrophages (BMDMs), enabling multiomic dissection of NF-{kappa}B pathway regulation in response to lipopolysaccharide (LPS) stimulation. Together, these findings demonstrate the broad impact that advancements in live-cell, paired multimodal technologies, especially upon perturbation, may offer in deepening our understanding of cellular biology.

cell biology↗

bioRxiv2020

A molecular atlas of proximal airway identifies subsets of known airway cell types revealing details of the unique molecular pathogenesis of Cystic Fibrosis

Introduction/AbstractCystic fibrosis (CF) is a lethal autosomal recessive disorder that afflicts in excess of 70,000 people globally. People with CF experience multi-organ dysfunction resulting from aberrant electrolyte transport across polarized epithelia due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CF-related lung disease is by far the most significant determinant of morbidity and mortality. In this study we report results from a multi-institute consortium in which single cell transcriptomics were applied to define disease-related changes to the proximal airway of CF donors (n=19) undergoing transplantation for end-stage lung disease compared to the proximal airway of previously healthy lung donors (n=19). We found that all major airway epithelial cell types were conserved between control and CF donors. Disease-dependent differences were observed, including an overabundance of epithelial cells transitioning to specialized ciliated and secretory cell subtypes coupled with an unexpected decrease in cycling basal cells. This study developed a molecular atlas of the proximal airway epithelium that will provide insights for the development of new targeted therapies for CF airway disease.

cell biology↗

bioRxiv2019

TGFβ superfamily signaling regulates the state of human stem cell pluripotency and competency to create telencephalic organoids

Telencephalic organoids generated from human pluripotent stem cells (hPSCs) are emerging as an effective system to study the distinct features of the developing human brain and the underlying causes of many neurological disorders. While progress in organoid technology has been steadily advancing, many challenges remain including rampant batch-to-batch and cell line-to-cell line variability and irreproducibility. Here, we demonstrate that a major contributor to successful cortical organoid production is the manner in which hPSCs are maintained prior to differentiation. Optimal results were achieved using fibroblast-feeder-supported hPSCs compared to feeder-independent cells, related to differences in their transcriptomic states. Feeder-supported hPSCs display elevated activation of diverse TGF{beta} superfamily signaling pathways and increased expression of genes associated with naive pluripotency. We further identify combinations of TGF{beta}-related growth factors that are necessary and together sufficient to impart broad telencephalic organoid competency to feeder-free hPSCs and enable reproducible formation of brain structures suitable for disease modeling. HIGHLIGHTSO_LIhPSC maintenance conditions influence outcomes in cortical organoid formation C_LIO_LIIdentification of an intermediate pluripotency state optimal for cortical organoids C_LIO_LIFeeder support involves activation of diverse TGF{beta} signaling pathways C_LIO_LIThe organoid-promoting effects of feeders can be mimicked by a TGF{beta} factor mixture C_LI

developmental biology↗

bioRxiv2018

A single cell transcriptomic analysis of human neocortical development

Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.\n\nOne Sentence SummaryComprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk.

neuroscience↗