Genetic barcodes are increasingly used to track individual cells and to quantitatively assess their clonal contributions over time. Although barcode quantification relies entirely on counting sequencing reads, detailed studies about the method's accuracy are still limited. We report on a systematic investigation of the relation between barcode abundance and resulting read counts after amplification and sequencing using cell-mixtures that contain barcodes with known frequencies ("miniBulks"). We evaluated the influence of protocol modifications to identify potential sources of error and elucidate possible limitations of the quantification approach. Based on these findings we designed an advanced barcode construct (BC32) to improved barcode calling and quantification, and to ensure a sensitive detection of even highly diluted barcodes. Our results emphasize the importance of using curated barcode libraries to obtain interpretable quantitative data and underline the need for rigorous analyses of any utilized barcode library in terms of reliability and reproducibility.
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http://dx.doi.org/10.1038/srep43249 | DOI Listing |
Anal Chim Acta
February 2025
Laboratório de Sensores Químicos Portáteis, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-861, Brazil. Electronic address:
Colorimetric paper-based analytical devices (CPADs) are cost-efficient and high-throughput technologies that use readily available materials for point-of-need (PON) applications by leveraging color changes in response to target analytes. However, the complexity of samples can limit the precision and accuracy of CPAD applications. Therefore, CPADs have been combined with chemometric approaches to enhance analytical performance and provide simple solutions to complex systems.
View Article and Find Full Text PDFPLoS One
January 2025
SLAC National Accelerator Laboratory, Stanford University, Stanford, California, United States of America.
Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Applied Chemistry, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
The integration of barcode technology with smartphones on paper-based analytical devices (PADs) presents a promising approach to bridging manual detection with digital interpretation and data storage. However, previous studies of 1D barcode approaches have been limited to providing only a "yes/no" response for analyte detection. Herein, a method of using barcode readout for semiquantitative signal detection on PADs has been achieved through the integration of barcode technology with a distance-based measurement concept on PADs.
View Article and Find Full Text PDFAnal Chim Acta
January 2025
Hunan Provincial Key Laboratory of Cytochemistry, School of Chemistry and Chemical Engineering, Changsha University of Science and Technology, Changsha, 410114, PR China.
Alkaline phosphatase (ALP) is a critical biomarker associated with various physiological and pathological processes, making its detection essential for disease diagnosis and biomedical research. In this study, we developed a novel, simple, and portable visual quantification method for ALP activity in cells using an efficient CuZnS nanomaterial with peroxidase-like properties, integrated into a smartphone-based platform for enhanced usability. The CuZnS nanomaterial catalyzes the breakdown of H₂O₂, generating ·OH radicals that oxidize the colorless substrate TMB into blue oxTMB, which is subsequently reduced back to TMB by ascorbic acid (AA).
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Simulation and Graphics, Faculty of Computer Science, University of Magdeburg, Universitätsplatz 2 39106, Magdeburg, Germany; Department of Computational Medicine, Ilmenau University of Technology, Germany.
Purpose: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segmentation of the intermuscular fat.
Method: Based on a self-created data set of 130 patients, an extended Unet structure was trained and evaluated with the help of Dice score, IoU and Pixel Accuracy. In addition, the interobserver variability for the decision between ground truth and post-processed segmentation was calculated to illustrate the relevance in everyday clinical practice.
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