Ling Li

Gene therapy using recombinant adeno-associated viral (AAV) vectors is a promising approach for treating inherited diseases. Precise characterization of AAV vector genomes and transcripts is essential for further optimization of this technique. Current visualization methods require multiple assays for detecting DNA and RNA, often involving mRNA-to-cDNA conversion. This can obscure insights into spatial distributions, particularly when AAV DNA and mRNA exhibit divergent trends. To address this challenge, we developed a padlock probe (PLP)-based rolling-circle amplification (RCA) technique. Using SplintR DNA ligase, which ligates single-stranded DNA splinted by complementary RNA sequences, enabled our method to directly target AAV mRNA without requiring conversion to cDNA, as well as genomic DNA. Incorporation of an intron within the transgene sequence allows probe designs that distinguish transgene DNA from its mRNA. This strategy enables specific detection of AAV single-stranded DNA (+), single-stranded DNA (−), and mRNA, each effectively amplified by PLP-RCA. Furthermore, this approach enables us to differentiate AAV single-stranded from double-stranded DNA by a combined treatment of lambda exonuclease and restriction enzyme digestion, providing the possibility of tracking the AAV genome processing following transduction. In transduced HeLa cells and liver tissues from AAV-injected mice, PLP-RCA revealed distinct temporal patterns of AAV DNA and mRNA localization, uncovering early DNA instability that influences transduction efficiency. This technique provides a robust and versatile platform for spatially resolved, single-cell analysis of AAV genome and transcript dynamics, facilitating a deeper understanding of AAV biology and aiding the optimization of vector-based gene therapies.

Cell counting plays a vital role in biomedical researches. However, manual cell counting is time‐consuming, laborious, and low efficiency and has a high counting error rate problem. An automatic counting approach for Hela cells of phase‐contrast microscopic images is proposed based on the combination of Otsu and watershed segmentation methods to solve the mentioned issues. Firstly, image preprocessing is performed. Secondly, the Otsu method was used to obtain an automatic global optimal threshold for segmentation to achieve batch counting of images. Thirdly, the marker watershed was performed to separate adherent cells and to avoid over‐segmentation simultaneously. Finally, cells in phase‐contrast microscopic images were counted by detecting the numbers of connected domains in the binary image. Taking the manual counting result as the counting standard and MIS, INC, and ACC are used as evaluation indicators. The experimental results showed that the average values of MIS, INC, and ACC of the proposed method are only 3.31%, 3.49%, and 96.69%, respectively. Additionally, each cell image was counted only takes 0.65 s on averagely. To further test the performance of the proposed method, a comparative experiment was carried out by Image J, and the result shows that the proposed method has a better counting performance with a higher average accuracy of 96.55% to Image J with 93.39%.The proposed method for cell counting is simple, feasible, fast and high accurate, and it can be used as an effective method for cell counting of the phase‐contrast microscopic images.