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This paper presents nonnegative spatial factorization, a general framework for spatially aware and interpretable dimension reduction for high-dimensional spatial data, and its application to spatial transcriptomics analysis.
A statistical approach for optimal design of multiplexed imaging studies has been developed. It determines experimental parameters that facilitate cell phenotype identification.
During segmentation of neurons in electron microscopy datasets, auxiliary learning via the prediction of local shape descriptors increases efficiency, which is important for the processing of datasets of ever-increasing size.
Nano3P-seq presents a nanopore-based sequencing tool to profile polyA-tailed and non-polyA-tailed transcripts, as well as capture polyA tail length and composition.
Localization Model Fit (LocMoFit) is an open-source tool for extracting meaningful parameters from individual structures in localization microscopy data. The framework was used for quantitative analysis of diverse biological structures.
This paper shows that the uniformity of vitreous ice thickness relies on the surface flatness of the supporting film, and presents a method to use ultraflat graphene as the support for cryo-EM specimen preparation.
DEDAL is a deep learning-based protein sequence alignment method that improves the quality of predicted alignment for remote homologs and better discriminates remote homologs from evolutionarily unrelated sequences.
Chemically activated protein domains are chemogenetic tools that inhibit the activity of short peptides in the absence of a small molecule. Their versatility was demonstrated on a range of peptides and in both cells and mice.
The parallel cryo electron tomography (PACE-tomo) method increases the throughput on in situ samples by parallelizing acquisition. It maximizes the usable sample area on individual lamellae without compromising data quality.
This work presents Prox-seq that couples sequencing and proximity ligation assay to simultaneously measure extracellular proteins, protein–protein interactions and mRNA in single cells.
Image-seq isolates cells from specific tissue locations under image guidance for analysis by single-cell RNA sequencing. The technique can be combined with in vivo imaging to document the temporal and dynamic history of the cells prior to sequencing.
This work presents m6Anet, which implements a neural-network-based multiple instance learning model to detect m6A modifications from direct RNA sequencing data.
Cellpose 2.0 improves cell segmentation by offering pretrained models that can be fine-tuned using a human-in-the-loop training pipeline and fewer than 1,000 user-annotated regions of interest.
The engineered hyperfolder YFP (hfYFP) and variants offer unprecedented chemical and thermal stability, making them versatile probes for microscopy as well as for challenging applications like correlative light and electron microscopy and expansion microscopy.