Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 µm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

Genomic editing in Burkholderia multivorans by CRISPR/Cas9.

Burkholderia cepacia complex bacteria have emerged as opportunistic pathogens in patients with cystic fibrosis and immunocompromised individuals, causing life-threatening infections. Because of the relevance of these microorganisms, genetic manipulation is crucial for explaining the genetic mechanisms leading to pathogenesis. Despite the availability of allelic exchange tools to obtain unmarked gene deletions in Burkholderia, these require a step of merodiploid formation and another of merodiploid resolution through two independent homologous recombination events, making the procedure long-lasting. The CRISPR/Cas9-based system could ease this constraint, as only one step is needed for allelic exchange. Here, we report the modification of a two-plasmid system (pCasPA and pACRISPR) for genome editing in Burkholderia multivorans. Several modifications were implemented, including selection marker replacement, the optimization of araB promoter induction for the expression of Cas9 and λ-Red system encoding genes, and the establishment of plasmid curing procedures based on the sacB gene or growth at a sub-optimal temperature of 18°C-20°C with serial passages. We have shown the efficiency of this CRISPR/Cas9 method in the precise and unmarked deletion of different genes (rpfR, bceF, cepR, and bcsB) from two strains of B. multivorans, as well as its usefulness in the targeted insertion of the gfp gene encoding the green fluorescence protein into a precise genome location. As pCasPA was successfully introduced in other Burkholderia cepacia complex species, this study opens up the possibility of using CRISPR/Cas9-based systems as efficient tools for genome editing in these species, allowing faster and more cost-effective genetic manipulation.IMPORTANCEBurkholderia encompasses different species of bacteria, some of them pathogenic to animals and plants, but others are beneficial by promoting plant growth through symbiosis or as biocontrol agents. Among these species, Burkholderia multivorans, a member of the Burkholderia cepacia complex, is one of the predominant species infecting the lungs of cystic fibrosis patients, often causing respiratory chronic infections that are very difficult to eradicate. Since the B. multivorans species is understudied, we have developed a genetic tool based on the CRISPR/Cas9 system to delete genes efficiently from the genomes of these strains. We could also insert foreign genes that can be precisely placed in a chosen genomic region. This method, faster than other conventional strategies based on allelic exchange, will have a major contribution to understanding the virulence mechanisms in B. multivorans, but it can likely be extended to other Burkholderia species.

Precision-engineered biomimetics: the human fallopian tube.

The fallopian tube has an essential role in several physiological and pathological processes from pregnancy to ovarian cancer. However, there are no biologically relevant models to study its pathophysiology. The state-of-the-art organoid model has been compared to two-dimensional tissue sections and molecularly assessed providing only cursory analyses of the model's accuracy. We developed a novel multi-compartment organoid model of the human fallopian tube that was meticulously tuned to reflect the compartmentalization and heterogeneity of the tissue's composition. We validated this organoid's molecular expression patterns, cilia-driven transport function, and structural accuracy through a highly iterative platform wherein organoids are compared to a three-dimensional, single-cell resolution reference map of a healthy, transplantation-quality human fallopian tube. This organoid model was precision-engineered to match the human microanatomy. One sentence summary: Tunable organoid modeling and CODA architectural quantification in tandem help design a tissue-validated organoid model.