Biology subjects

Bui, J.

Publications and source records attributed to Bui, J..

6 recordsLinked to original sources

bioRxiv2026

Accurate and scalable demultiplexing of single-cell RNA sequencing using BEACON

Barcode-based multiplexing strategies can significantly increase sample throughput and decrease costs, while mitigating batch effects of single-cell RNA sequencing experiments. However, these approaches can be limited by inaccurate or inefficient demultiplexing, resulting in cell loss and reduced statistical power. Here, we present BEACON, a novel sample demultiplexing method that efficiently learns the background count distribution from data and removes it from individual cells, thereby improving classification accuracy. BEACON outperforms other state-of-the-art methods on multiple human data sets. We apply it to cancer cell line time course experiments in vitro, enabling the identification of genes associated with aggressive tumors in vivo. Finally, we adapt BEACON to multimodal protein-transcriptome profiling, enhancing protein signal recovery to identify a CD161-positive effector memory CD4 T-cell population with a Th17-like phenotype, which we prospectively validate. BEACON can therefore be applied to other droplet-based single-cell sequencing methodologies.

bioinformatics↗

bioRxiv2024

An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues

This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers [~]90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types. Graphical abstract created with biorender.com O_FIG O_LINKSMALLFIG WIDTH=152 HEIGHT=200 SRC="FIGDIR/small/591396v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1cba4c1org.highwire.dtl.DTLVardef@adb231org.highwire.dtl.DTLVardef@18db263org.highwire.dtl.DTLVardef@1babde0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗

bioRxiv2020

HIV-specific T-cell responses reflect substantive in vivo interactions with infected cells despite long-term therapy

Antiretroviral therapies (ART) durably suppress HIV replication to undetectable levels - however, infection persists in the form of long-lived reservoirs of infected cells with integrated proviruses, that re-seed systemic replication if ART is interrupted. A central tenet of our current understanding of this persistence is that infected cells are shielded from immune recognition and elimination through a lack of antigen expression from proviruses. Efforts to cure HIV infection have therefore focused on reactivating latent proviruses to enable immune-mediated clearance, but these have yet to succeed in driving reductions in viral reservoirs. Here, we revisited the question of whether HIV reservoirs are predominately immunologically silent from a new angle, by querying the dynamics of HIV-specific T-cell responses over long-term ART for evidence of ongoing recognition of HIV-infected cells. We show that T-cell responses to autologous reservoir viruses persist over years, and that the maintenance of HIV-Nef-specific responses was uniquely associated with residual frequencies of infected cells. These responses disproportionately exhibited a cytotoxic, effector functional profile, indicative of recent in vivo recognition of HIV-infected cells. These results indicate substantial visibility of the HIV reservoir to T-cells on stable ART, presenting both opportunities and challenges for the development of therapeutic approaches to curing HIV infection.

immunology↗

bioRxiv2019

PD-L1:CD80 Heterodimer Triggers CD28 While Repressing Both PD-1 and CTLA4 Pathways

Combined immunotherapy with anti-PD-1/PD-L1 and anti-CTLA4 has resulted in superior clinical responses compared to single agent therapy. The underlying mechanisms for this synergy have yet to be elucidated and investigations have largely focused on cellular interactions. Herein, we report a molecular crosstalk in which the PD-1 ligand PD-L1 and the CTLA4 ligand CD80 heterodimerize in cis. This heterodimerization blocks both PD-L1:PD-1 and CD80:CTLA4 interactions, but preserves the ability of CD80 to activate the T cell costimulatory receptor CD28. Remarkably, PD-L1 expression on antigen presenting cells (APCs) protects CD80 from CTLA4 mediated trans-endocytosis, and the therapeutic PD-L1 blockade antibody atezolizumab paradoxically downregulates CD80 on APCs, presumably reducing its co-stimulatory ability. Importantly, this effect can be negated by co-blockade of CTLA4 with ipilimumab. Our study reveals an unexpected immune stimulatory role of cis-acting PD-L1 and a mechanism of anti-PD-L1/anti-CTLA4 crosstalk, providing a therapeutic rationale for combination blockade of PD-L1 and CTLA4.

immunology↗

bioRxiv2019

Mechanochemical coupling and junctional forces during collective cell migration

Cell migration, a fundamental physiological process in which cells sense and move through their surrounding physical environment, plays a critical role in development and tissue formation, as well as pathological processes, such as cancer metastasis and wound healing. During cell migration, dynamics are governed by the bidirectional interplay between cell-generated mechanical forces and the activity of Rho GTPases, a family of small GTP-binding proteins that regulate actin cytoskeleton assembly and cellular contractility. These interactions are inherently more complex during the collective migration of mechanically coupled cells, due to the additional regulation of cell-cell junctional forces. In this study, we present a minimal modeling framework to simulate the interactions between mechanochemical signaling in individual cells and interactions with cell-cell junctional forces during collective cell migration. We find that migration of individual cells depends on the feedback between mechanical tension and Rho GTPase activity in a biphasic manner. During collective cell migration, waves of Rho GTPase activity mediate mechanical contraction/extension and thus synchronization throughout the tissue. Further, cell-cell junctional forces exhibit distinct spatial patterns during collective cell migration, with larger forces near the leading edge. Larger junctional force magnitudes are associated with faster collective cell migration and larger tissue size. Simulations of heterogeneous tissue migration exhibit a complex dependence on the properties of both leading and trailing cells. Computational predictions demonstrate that collective cell migration depends on both the emergent dynamics and interactions between cellular-level Rho GTPase activity and contractility, and multicellular-level junctional forces.

biophysics↗