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All life sciences research is potentially subject to ethical considerations. Institutions should support collaborations with professional ethicists and philosophers to help life scientists navigate ethical crossroads.
FICTURE software addresses a critical challenge in spatial omics analysis: making high-resolution inference with only a few molecules per square micron. This tool fully realizes the potential of contemporary spatial platforms by learning latent spatial factors from the whole transcriptome while preserving the resolution of each technology at scale.
The capability of high-resolution in situ imaging by electron cryo-tomography (cryo-ET) has now been expanded to large multicellular tissues by newly developed workflows involving lift-out and serial sectioning using focused ion beam milling under cryogenic conditions.
Serialized on-grid lift-in sectioning for tomography (SOLIST) improves the throughput of the serial lift-out technique for creating lamellas, addressing a major bottleneck in the use of cryo-electron tomography for in situ structural biology.
FICTURE is a segmentation-free approach for identifying tissue architecture in spatial transcriptomics data. FICTURE is compatible with both imaging-based and sequencing-based methods and is uniquely suited for handling the largest available datasets.
SN2N, a Self-inspired Noise2Noise module, offers a versatile solution for volumetric time-lapse super-resolution imaging of live cells. SN2N uses self-supervised data generation and self-constrained learning for training with a single noisy frame.
This work introduces a k-mer-based approach to customizing a pangenome reference, making it more relevant to a new sample of interest. This method enhances the accuracy of genotyping small variants and large structural variants.
Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE) is a robust feature extraction and classification pipeline for diverse and heterogeneous structures in both 2D and 3D single-molecule localization microscopy data.
MiLoPYP is a two-step, dataset-specific contrastive learning-based method for fast and accurate detection and localization of a diverse range of target structures in cryo-electron tomography data, enabling improved in situ structural biology.
Point spread function (PSF) splitting with the ‘Circulator’, which encodes the fluorophore emission band into the PSF, improves the information content of fluorescence microscopy and enables improved super-resolution imaging and single-particle tracking.
Collaborative augmented reconstruction (CAR) is a platform for large-scale reconstruction of neurons and other cells from multi-dimensional imaging datasets. It can be accessed from a variety of devices simultaneously for efficient and accurate reconstruction.
Spacia is a multiple-instance learning model for cell–cell communication (CCC) interference in single-cell resolution spatially resolved transcriptomics data. Spacia can map complex CCCs by modeling cell proximity and CCC-driven gene perturbation.
