Nature Methods最新文献

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE’s cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.

Navigate enables biologists and technology developers alike to establish and reuse smart microscopy pipelines on diverse sets of hardware from within a single framework. While generalizable Python-based frameworks for smart microscopy have been built, they were designed for stimulated emission depletion⁵ or single-molecule localization microscopy⁶ and do not yet address LSFM’s specific acquisition challenges, including decoupled illumination and detection optomechanics and a lack of an optical substrate for focus maintenance. While GUI-based frameworks for image postprocessing exist⁶, to the authors’ knowledge, navigate is the only software that enables decision-based acquisition routines to be generated in a code-free format (Supplementary Table 1).

A schematic of navigate’s software architecture is presented in Supplementary Fig. 1. The plug-in architecture of navigate facilitates the addition of new hardware, enabling users to integrate otherwise unsupported devices. For image-based feedback, custom analysis routines can also be loaded within navigate’s environment to evaluate images stored as NumPy arrays in memory. Navigate supports the addition of REST-API interfaces for two-way communication with image analysis programs running outside of Python or in different Python environments, such as Ilastik⁷, enabling developers to make calls to state-of-the-art software while avoiding dependency conflicts. Image-based feedback can be leveraged to perform diverse tasks, such as sensorless adaptive optics in optically complex specimens (Fig. 1c). We believe this flexibility is necessary for the software to accommodate the diverse modalities of LSFM and to integrate feedback mechanisms.

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.

Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit (https://github.com/vgteam/vg) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods.