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Single-cell RNA sequencing (scRNA-seq) using metabolic RNA labeling enables detailed analysis of dynamic gene expression within single cells. However, most studies are limited to in vitro settings, restricting the exploration of in vivo transcriptomic dynamics. To address this, we developed scDyna-seq, a time-resolved scRNA-seq method for in vivo applications using 4-thiouridine (4sU) labeling. scDyna-seq efficiently captures nascent RNA, allowing for precise tracking of gene expression in both in vitro and in vivo contexts, including crossing the blood-brain and blood-fetal barriers. It is also compatible with other single-cell multiomics approaches. In a mouse bladder cancer model, scDyna-seq revealed that cisplatin (-diaminodichloroplatinum, CDDP) induced significant dynamic changes in tumor-infiltrating lymphocytes, particularly in genes related to costimulation, effector functions, and exhaustion, which were not detected by conventional methods. When coupled with scTCR-seq, scDyna-seq showed increased TCR clonal expansion linked to CDDP-induced immunogenic death and neoantigen production. In conclusion, scDyna-seq offers safe, precise in vivo RNA labeling as well as single-cell analysis, expanding our understanding of cellular dynamics and facilitating research in health and disease.
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http://dx.doi.org/10.1021/acs.analchem.4c05648 | DOI Listing |
Front Microbiol
August 2025
BIOASTER, Lyon, France.
We propose an innovative technology to classify the Mechanism of Action (MoA) of antimicrobials and predict their novelty, called HoloMoA. Our rapid, robust, affordable and versatile tool is based on the combination of time-lapse Digital Inline Holographic Microscopy (DIHM) and Deep Learning (DL). In combination with hologram reconstruction.
View Article and Find Full Text PDFBiol Psychiatry Glob Open Sci
November 2025
University of Basel, Department of Clinical Research (DKF), University Psychiatric Clinics, Translational Neurosciences, Basel, Switzerland.
Background: The hippocampus plays a critical role in psychosis, with reduced volume observed across the psychosis continuum. These structural changes are associated with cognitive deficits, symptom severity, and increased risk of psychosis progression. Elevated hippocampal perfusion and glutamate/GABA (gamma-aminobutyric acid) imbalance further suggest metabolic dysregulation as a key mechanism.
View Article and Find Full Text PDFJHEP Rep
October 2025
Janssen Pharmaceutica NV, Beerse, Belgium.
Background & Aims: Previous studies showed that combination treatment with short interfering RNA JNJ-73763989 (JNJ-3989) ± capsid assembly modulator bersacapavir (JNJ-56136379) and nucleos(t)ide analogs (NAs) was well tolerated by patients with chronic HBV (CHB), with JNJ-3989 dose-dependent reductions in viral markers, including HBsAg. The open-label, single-arm phase IIa PENGUIN study (NCT04667104) evaluated this regimen plus pegylated interferon alpha-2a (PegIFN-α2a) in patients with virologically suppressed CHB.
Methods: Patients who were either HBeAg-positive or -negative virologically suppressed and taking NAs were included; all received JNJ-3989 ± bersacapavir for 24 weeks (some either did not start or discontinued bersacapavir as a result of protocol amendment) with PegIFN-α2a added during the final 12 weeks of treatment.
Front Immunol
September 2025
Department of Clinical Oncology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Background: Neoantigen-based vaccines show promising therapeutic potential in solid tumors such as melanoma, GBM, NSCLC, and CRC. However, clinical responses remain suboptimal in stage IV patients, due to ineffective T-cell function and high tumor burdens. To overcome these limitations, our study investigates a combination strategy using neoantigen peptide vaccines and precision critical lesion radiotherapy (CLERT), which delivers immunomodulatory doses to key tumor regions synergistically enhance immune activation and inhibit progression in multifocal stage IV patients.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets.
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