Biology subjects

Good, Z.

Publications and source records attributed to Good, Z..

8 recordsLinked to original sources

bioRxiv2026

All-by-All Cytokine Receptor Pairing Network Unlocks Coding of Non-Natural T Cell States

Cytokine receptor pairing rules, set by evolution, confine JAK-STAT signaling to a narrow region of a far larger combinatorial space. Of more than 1,200 pairings theoretically possible among the [~]36 JAK-associated human cytokine receptors, only [~]30-40 exist in nature. Using a double-orthogonal platform, we enforced pairings across the full all-by-all receptor matrix and resolved a fine-grained STAT atlas richer than the natural repertoire. Selected non-natural pairings generated emergent T cell states unpredictable from either parental receptor, with pairing orientation encoding signaling specificity. A synthetic IL-21R>IL-2R{beta} pairing, but not its reciprocal, drove a cytotoxic Tc17-like state, whereas natural IL-9R/{gamma}c drove a Tc1 fate despite similar STAT activation, showing that rebalancing quantitative STAT combinatorics can modulate T cell fate. Recombining IL-31R, not expressed in T cells, with STAT-biased receptor partners generated diverse states, several with superior antitumor efficacy. These findings define a non-natural pairing code for engineering synthetic T cell fates.

immunology↗

bioRxiv2026

Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy

Chimeric antigen receptor (CAR) T cell therapies have reshaped treatment for cancers and immune-mediated diseases, yet their safety and efficacy depend on both the proliferation of engineered cells and their dynamic functional state -- features that remain challenging to monitor in real-time clinical settings. Current methods require targeted labels, extensive processing, and provide only static snapshots of cell identity and activation. Here, we introduce a surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) approach that enables single-cell identification of engineered CAR T cells without molecularly-targeted labels and time-resolved, semi-continuous monitoring of their functional activation state through a single physical readout spanning donor-derived cells and patient blood. From intrinsic vibrational signatures of live cells, we detect spectral differences resulting from engineered receptor expression in donor-derived CD19- and GD2-targeted CAR T cells (nine and five donors, respectively) with 81-85% donor-level accuracy, and resolve dynamic antigen-specific activation trajectories with temporal precision. Applying this same SERS-ML method to longitudinal samples from a four-patient CD19-CAR T therapy cohort, we classify patient peripheral blood mononuclear cells across pre-and post-infusion timepoints with a mean patient-level accuracy of 84%, and show that isolated CAR-positive T cells are distinguishable from both CAR-negative T cells and background populations with average 86-88% accuracies. These capabilities stem from biochemical signatures consistent with processes such as receptor expression, tonic signalling, and immune synapse formation, demonstrating a single method that reports both cellular identity and activation state with biochemical specificity across cell engineering and clinical monitoring contexts. Our results extend CAR T cell monitoring beyond static phenotyping and support the potential of SERS-ML analysis for rapid, point-of-care assessment of engineered immune cells across the therapeutic lifecycle.

bioengineering↗

bioRxiv2025

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics Data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within the same cell. Most existing integration methods are optimized for transcriptomic data and rely on a large set of shared, strongly linked features, an assumption that often fails for low-dimensional proteomic modalities. We present CellFuse, a deep learning-based, modality-agnostic integration framework designed specifically for settings with limited feature overlap. CellFuse leverages supervised contrastive learning to learn a shared embedding space, enabling accurate cell type prediction and seamless integration across modalities and experimental conditions. Across a range of datasets including healthy PBMCs, bone marrow, CAR-T-treated lymphoma, and healthy and tumor tissues--CellFuse consistently outperforms existing methods in both integration quality and runtime efficiency. It maintains high accuracy even in the presence of missing markers and rare cell types, and performs robustly in cross-dataset comparisons, making it a powerful tool for scalable and high-fidelity single-cell data integration in basic and translational research.

bioinformatics↗

bioRxiv2025

MONTAGE: A Computation Framework to Identify Spatially Resolved Functional Enrichment Gradients in the Tissue Microenvironment via Spatial Communities

Single-cell spatial-omics has advanced our understanding of how tumor microenvironment contributes to cancer progression. However, most single-cell spatial-omics studies focus on cell types and neighborhoods, offering limited information about functional and clinical relevance of cellular organization. We introduce MONTAGE, a computational framework to reconstruct, functionally analyze, and identify clinically relevant cellular spatial communities (SCs). MONTAGE generates a gene signature matrix of SCs from integrated atlases that offer single-cell resolution with transcriptomics, and reconstructs tissue maps ("montages"), reflecting sequential SC compositional changes along spatial gradients of biological function enrichments. The MONTAGE signature matrix can also be used to deconvolve bulk and spot-based spatial transcriptomic data, and combining with clinical metadata, allows assessing SC clinical relevance. In a head and neck cancer study, MONTAGE reveals SCs enriched with malignant cells and granulocytes shifting to SCs enriched with macrophages and fibroblasts along gradient of epithelial-mesenchymal transition, and clinical prognostic significance of the SCs.

systems biology↗

bioRxiv2023

Engineered CD47 protects T cells for enhanced antitumor immunity

Adoptively transferred T cells and agents designed to block the CD47/SIRP axis are promising antitumor therapeutics, which activate distinct arms of the immune system. We administered anti-CD47 (CD47) with adoptively transferred T cells with the goal of enhancing antitumor efficacy but observed rapid macrophage-mediated clearance of T cells expressing chimeric antigen receptors (CARs) or engineered T cell receptors, which blunted therapeutic benefit. CD47 mediated CAR T clearance was potent and rapid enough to serve as an effective safety switch. To overcome this challenge, we engineered a CD47 variant (47E) that engaged SIRP and provided a "dont-eat-me" signal that was not blocked by CD47 antibodies. TCR or CAR T cells expressing 47E were resistant to clearance by macrophages following CD47, and mediated significant, sustained macrophage recruitment into the TME. Although many of the recruited macrophages manifested an M2-like profile, the combined therapy resulted in synergistic enhancement in antitumor efficacy. This work identifies macrophages as major regulators of T cell persistence and illustrates the fundamental challenge of combining T cell directed therapeutics with those designed to activate macrophages. It further delivers a therapeutic approach capable of simultaneously harnessing the antitumor effects of T cells and macrophages that manifests markedly enhanced potency against solid tumors.

bioengineering↗

bioRxiv2022

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cells protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

bioinformatics↗

bioRxiv2020

Transient "rest" induces functional reinvigoration and epigenetic remodeling in exhausted CAR-T cells

T cell exhaustion limits immune responses against cancer and is a major cause of resistance to CAR-T cell therapeutics. Using a model wherein tonic CAR signaling induces hallmark features of exhaustion, we employed a drug-regulatable CAR to test the impact of transient cessation of receptor signaling (i.e. "rest") on the development and maintenance of exhaustion. Induction of rest in exhausting or already-exhausted CAR-T cells resulted in acquisition of a memory-like phenotype, improved anti-tumor functionality, and wholescale transcriptional and epigenetic reprogramming. Similar results were achieved with the Src kinase inhibitor dasatinib, which reversibly suppresses CAR signaling. The degree of functional reinvigoration was proportional to the duration of rest and was associated with expression of transcription factors TCF1 and LEF1. This work demonstrates that transient cessation of CAR-T cell signaling can enhance anti-tumor potency by preventing or reversing exhaustion and challenges the paradigm that exhaustion is an epigenetically fixed state.

immunology↗

bioRxiv2019

c-Jun Overexpressing CAR-T Cells are Exhaustion-Resistant and Mediate Enhanced Antitumor Activity

CAR T cells mediate antitumor effects in a small subset of cancer patients, but dysfunction due to T cell exhaustion is an important barrier to progress. To investigate the biology of exhaustion in human T cells expressing CAR receptors, we used a model system employing a tonically signaling CAR, which induces hallmarks of exhaustion described in other settings. Exhaustion was associated with a profound defect in IL-2 production alongside increased chromatin accessibility of AP-1 transcription factor motifs, and overexpression of numerous bZIP and IRF transcription factors that have been implicated in inhibitory activity. Here we demonstrate that engineering CAR T cells to overexpress c-Jun, a canonical AP-1 factor, enhanced expansion potential, increased functional capacity, diminished terminal differentiation and improved antitumor potency in numerous in vivo tumor models. We conclude that a functional deficiency in c-Jun mediates dysfunction in exhausted human T cells and that engineering CAR T cells to overexpress c-Jun renders them exhaustion-resistant, thereby addressing a major barrier to progress for this emerging class of therapeutics.

immunology↗