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Author Correction: Single-cell guided prenatal derivation of primary fetal epithelial organoids from human amniotic and tracheal fluids.
Nat Med
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells.
Brief Bioinform
November 2024
Department of Automation, Xiamen University, Xiang'an South Road, Xiang'an, 361102, Xiamen, Fujian, China.
Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference.
View Article and Find Full Text PDFBimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.
Adv Sci (Weinh)
January 2025
Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics， Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors， Guangdong Provincial Key Laboratory of Single Cell Technology and Application, School of Laboratory Medicine and Biotechnology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in disease regulation. However, the practical application of EV-circRNAs in the early diagnosis of GC is yet to be thoroughly explored due to the low accuracy of EV-circRNAs analysis.
View Article and Find Full Text PDFIntegrating single-cell multimodal epigenomic data using 1D convolutional neural networks.
Bioinformatics
December 2024
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.
Motivation: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using these types of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type.
View Article and Find Full Text PDFUnveiling the role of MDH1 in breast cancer drug resistance through single-cell sequencing and schottenol intervention.
Cell Signal
January 2025
Department of Breast and Thyroid Surgery, The Qinghai Provincial People's Hospital, Xining 810007, China. Electronic address:
This study utilizes single-cell RNA sequencing data to reveal the transcriptomic characteristics of breast cancer and normal epithelial cells. Nine significant cell populations were identified through stringent quality control and batch effect correction. Further classification of breast cancer epithelial cells based on the PAM50 method and clinical subtypes highlighted significant heterogeneity between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (NTNBC).
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