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Micro-kiss (μkiss) is a micropipette-based approach for delivering very small amounts of nanoparticles and small molecules to the cell surface with exquisite spatiotemporal control, enabling a wide range of biological investigations.
Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) offers a general framework for estimating uncertainty for spatial gene expression predictions, enabling improved downstream analysis of spatially resolved transcriptomics data.
Metrics Reloaded is a comprehensive framework for guiding researchers in the problem-aware selection of metrics for common tasks in biomedical image analysis.
CombFold is a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2.
We pinpoint PCR artifacts as the primary source of inaccurate quantification in both short- and long-read RNA sequencing, a problem that intensifies with an increase in PCR cycles in both bulk and single-cell sequencing contexts. To overcome this challenge, we engineered a novel unique molecular identifier (UMI) barcode composed of homotrimer nucleotide blocks. This design facilitates accurate quantification of RNA molecules, substantially improving molecular counting.
This study introduces a method utilizing homotrimeric nucleotide blocks to achieve accurate counts of RNA molecules in both bulk and single-cell sequencing data.
We developed a prime editing (PE) strategy by incorporating a 5′–3′ exonuclease activity, which enhanced the efficacy and precision of ≥30-nucleotide DNA insertions without a secondary nick. Our optimization of the PE complex revealed that recruiting the exonuclease via an RNA aptamer outperformed direct protein fusions.
Intrinsically disordered regions of proteins are prevalent across the kingdoms of life; however, biophysical characterization is expensive, requiring specialized expertise and equipment and time-consuming sample preparation. By combining simulations and deep learning, we have developed a method to predict their average ensemble properties directly from sequence.
ALBATROSS is a deep-learning-based model for predicting ensemble properties of intrinsically disordered proteins and protein regions, such as radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences.
Improvements to the fully genetically encoded Neonothopanusnambi bioluminescence pathway enhance autobioluminescence by up to two orders of magnitude in plants and other species, enabling novel applications of bioluminescence imaging in biology.
A square electron beam improves imaging of large fields of view in transmission electron microscopes by facilitating montage tomography of vitrified specimens with no loss in data quality relative to conventional round beams.
RNA family sequence generator (RfamGen) is a deep generative model for designing novel, functional RNA sequences. RfamGen is applicable to diverse RNA families and can yield ribozymes with higher enzymatic activity.
Content-aware frame interpolation (CAFI) improves the temporal resolution in time-lapse imaging by accurately predicting images in between image pairs. By allowing fewer frames to be imaged, CAFI also enables gentler live-cell imaging.
DoTA-seq leverages a microfluidic droplet system to isolate and lyse diverse microbes and amplify target genetic loci, enabling high-throughput single-cell sequencing of microbial populations.