Engineered pegRNAs improve prime editing efficiency.

Prime editing enables the installation of virtually any combination of point mutations, small insertions or small deletions in the DNA of living cells. A prime editing guide RNA (pegRNA) directs the prime editor protein to the targeted locus and also encodes the desired edit. Here we show that degradation of the 3' region of the pegRNA that contains the reverse transcriptase template and the primer binding site can poison the activity of prime editing systems, impeding editing efficiency. We incorporated structured RNA motifs to the 3' terminus of pegRNAs that enhance their stability and prevent degradation of the 3' extension. The resulting engineered pegRNAs (epegRNAs) improve prime editing efficiency 3-4-fold in HeLa, U2OS and K562 cells and in primary human fibroblasts without increasing off-target editing activity. We optimized the choice of 3' structural motif and developed pegLIT, a computational tool to identify non-interfering nucleotide linkers between pegRNAs and 3' motifs. Finally, we showed that epegRNAs enhance the efficiency of the installation or correction of disease-relevant mutations.

Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for Large-Scale Calcium Imaging Datasets.

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

Rapid Antibiotic Susceptibility Testing of Uropathogenic E. coli by Tracking Submicron Scale Motion of Single Bacterial Cells.

To combat antibiotic resistance, a rapid antibiotic susceptibility testing (AST) technology that can identify resistant infections at disease onset is required. Current clinical AST technologies take 1-3 days, which is often too slow for accurate treatment. Here we demonstrate a rapid AST method by tracking sub-µm scale bacterial motion with an optical imaging and tracking technique. We apply the method to clinically relevant bacterial pathogens, Escherichia coli O157: H7 and uropathogenic E. coli (UPEC) loosely tethered to a glass surface. By analyzing dose-dependent sub-µm motion changes in a population of bacterial cells, we obtain the minimum bactericidal concentration within 2 h using human urine samples spiked with UPEC. We validate the AST method using the standard culture-based AST methods. In addition to population studies, the method allows single cell analysis, which can identify subpopulations of resistance strains within a sample.

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.