Biology subjects

Sung, E.

Publications and source records attributed to Sung, E..

6 recordsLinked to original sources

bioRxiv2026

A Bayesian framework reveals heterogeneous and stochastic decision-making in NK cell cytotoxicity

Natural killer (NK) cells display striking variability in their cytotoxic responses, but whether this variation reflects random events, stable differences between cells, or effects of previous encounters remains unclear. We developed a Bayesian framework that uses single-cell interaction histories to disentangle these sources of variability. The framework models target encounters and killing decisions to quantify population heterogeneity, determine the sample sizes needed to distinguish competing mechanisms, and separate stable cell-to-cell differences from history-dependent behaviour. Synthetic data established when these mechanisms can be reliably distinguished in practice. We tested the framework using time-lapse imaging of NK cells exposed to rituximab or CC-96673, an antibody co-targeting CD20 and CD47. Despite similar overall killing, the framework identified distinct underlying responses: rituximab mainly increased mean killing rate, whereas CC-96673 reduced cell-to-cell variation. Event-count analysis supported continuous population heterogeneity, while ordered interaction histories revealed that both stable differences in killing propensity and previous encounters shape cytotoxic decisions. To make the framework directly usable, we introduce BARRACUDA, an open-source web platform and Python package implementing these analyses, including donor-aware extensions, for single-cell cytotoxicity datasets.

immunology↗

bioRxiv2024

Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas

Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene prediction. We address the limitations in de novo network inference from single-cell expression data through dropout imputation, metacells formation, and data transformation. Employing this data preprocessing pipeline, we inferred cell-type-specific co-expression links from single-cell atlas data, covering various cell types and tissues, and integrated over 850K of these inferred links into a preexisting human interactome, HumanNet, resulting in HumanNet-plus. This integration notably enhanced accuracy of network-based disease gene prediction. These findings suggest that with proper data preprocessing, network inference from single-cell gene expression data can be highly effective, potentially enriching the human interactome and advancing the field of network medicine.

bioinformatics↗

bioRxiv2024

Mitochondria Regulate the Cell Fate Decisions of Megakaryocyte-Erythroid Progenitors

Recent studies highlight the critical role of mitochondria in hematopoiesis, especially in stem cell function and erythroid maturation. To explore mitochondrial contributions to cell lineage commitment of hematopoietic progenitors, we utilized Cars2 mutant mice, an ideal model for this purpose. CARS2, a mitochondrial isoform of cysteinyl-tRNA synthetase, has cysteine persulfide synthase (CPERS) activity. Our new mouse model, with reduced CPERS activity, showed that the Cars2 mutation led to mitochondrial inhibition and anemia by suppressing erythroid commitment in megakaryocyte-erythroid progenitors (MEPs). This suppression was reproduced using mitochondrial electron transport chain inhibitors. We identified two distinct MEP populations based on the mitochondrial content: mitochondria-rich MEPs favored erythroid differentiation, while the mitochondria-poor MEPs favored megakaryocyte differentiation. These findings reveal critical contributions of mitochondria to the MEP lineage selection, acting as a "mitochondrial navigation" for lineage commitment.

cell biology↗

bioRxiv2024

Single-cell multi-omics reveals tumor microenvironment factors underlying poor immunotherapy responses in ALK-positive lung cancer

Anaplastic lymphoma kinase (ALK) rearrangement is a major oncogenic driver in non-small cell lung cancer (NSCLC). While ALK tyrosine kinase inhibitors have shown promising therapeutic effects, overcoming resistance with immunotherapy becomes necessary when resistance develops. However, various clinical trials have revealed that their efficacies remain limited. To investigate the tumor microenvironment (TME) factors contributing to poor immune checkpoint blockade responses in ALK-positive patients, we performed single-cell RNA and ATAC sequencing on lung adenocarcinoma (LUAD) tumors with and without ALK rearrangements. Integrative analysis with additional public LUAD cohorts revealed distinct immune landscapes in ALK-positive tumors, marked by enriched innate immunity and depleted adaptive immunity. ALK-positive malignant cells exhibit higher stemness and aggressive phenotype. Tumor-associated macrophages (TAMs) in these tumors predominantly maintain pro-tumoral M2-like states, reinforcing immune suppression. B cells show reduced immune reactivity and impaired tertiary lymphoid structure formation, while CD8+ T cells display bystander-like signatures and reduced tumor reactivity. Single-cell chromatin accessibility profiles combined with regulatory network analysis suggest that differences in transcription factor activities, rather than chromatin accessibility, may underlie T cell dysfunction. These findings provide insights into the immunosuppressive TME of ALK-positive LUAD, potentially explaining the failure of recent immunotherapy trials and highlighting targets for improving efficacy.

cancer biology↗

bioRxiv2023

Domain-Invariant Brainstem Nuclei Segmentation and Signal Quantification

AO_SCPLOWBSTRACTC_SCPLOWBrainstem nuclei are hard to distinguish due to very few distinctive features which makes detecting them with high accuracy extremely difficult. We introduce StARQ that builds on SeBRe, a deep learning-based framework to segment regions of interest. StARQ provides new functionalities for automated segmentation of brainstem nuclei at high granularity, and quantification of underlying neural features such as axonal tracings, and synaptic punctae. StARQ will serve as a toolbox for generalized brainstem analysis, enabling reliable high-throughput computational analysis with open-source models.

neuroscience↗

bioRxiv2020

A toolkit to generate inducible and interconvertible Drosophila transgenes

The existence of three independent binary systems for conditional gene expression (Gal4/UAS; LexA/LexAop; QF/QUAST) has greatly expanded versatile genetic analyses in the fruit fly Drosophila melanogaster; however, the experimental application of these tools is limited by the need to generate multiple collections of non-interchangeable transgenic fly strains for each inducible gene expression system. To address this practical limitation, we developed a modular vector that contains the regulatory elements from all three binary systems, enabling Gal4-, LexA- or QF-dependent expression of transgenes. Our methods also incorporate DNA elements that facilitate independent site-specific recombination and elimination of regulatory UAS, LexAop or QUAST modules with spatial and temporal control, thus offering unprecedented possibilities and logistical advantages for in vivo genetic modulation and efficient interconversion of overexpression transgenic fly lines.

genetics↗