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During the first two years of postnatal development, the human brain undergoes rapid, pronounced changes in size, shape and content. Using high-resolution MRI, we constructed month-to-month atlases of infants 2 weeks to 2 years old, capturing key spatiotemporal traits of early brain development in terms of cortical geometries and tissue properties.
We engineered a 3D outer-blood-retina-barrier (3D-oBRB) with a fully polarized retinal pigment epithelium (RPE) monolayer on top of a Bruch’s membrane and a fenestrated choriocapillaris network. This 3D-oBRB tissue faithfully recapitulates RPE– choriocapillaris interactions, dry age-related macular degeneration (AMD) phenotypes (including sub-RPE drusen deposits and choriocapillaris degeneration) and the wet AMD phenotype of choriocapillaris neovascularization.
Localization Model Fit (LocMoFit) is a tool that enables fitting of super-resolution microscopy data to an arbitrary geometric model. The fit extracts quantitative parameters of individual cellular structures, which can be used to investigate dynamic and heterogenous protein assemblies and to create average protein distribution maps.
We trained DEDAL, an algorithm based on deep-learning language models, to generate pairwise alignments of protein sequences taking into account the sequence-specific context of amino acid substitutions or gaps. DEDAL improved the alignment correctness on remote homologs by up to threefold and the discrimination of remote homologs from evolutionarily unrelated sequences.
We developed a FAIR (findable, accessible, interoperable, reusable) framework for researchers to share spatially standardized brain models. TemplateFlow enables the implementation of more reliable data processing pipelines by maximizing the accessibility of such models. It equips neuroimaging researchers with a foundational tool to bridge gaps between populations and species in neuroscience research.
The ability to measure protein complexes in single cells is currently limited to a very small number of targets. Combining a proximity ligation assay with single-cell sequencing creates the ability to measure hundreds of extracellular protein complexes and thousands of mRNAs in individual cells.
To accelerate data acquisition for in situ cryo-electron tomography, we created a method that takes into consideration sample geometry for the robust prediction of sample movement while the microscope stage is tilted. This approach enabled the parallel collection of tens to hundreds of tilt series.
By modeling the probability of N6-methyladenosine (m6A) RNA modifications for individual reads from direct RNA sequencing, m6Anet achieves high classification accuracy and takes a step towards transcriptome-wide maps of m6A modifications at single-base, single-molecule resolution.
Hyperfolder yellow fluorescent protein (hfYFP) and its variants are fluorescent proteins with high chemical and thermal stability. They resist aggregation, withstand diverse chemical challenges and show promise in expansion and electron microscopies. The chloride resistance and uncanny stability in guanidinium of hfYFP enable fluorescence-guided protein purification under denaturing conditions.
Common cellular segmentation models based on machine learning perform suboptimally for test images that differ greatly from training images. Cellpose 2.0 allows biologists to quickly train state-of-the-art segmentation models on their own imaging data. This was previously only possible using large, annotated datasets and required expert machine learning knowledge.
Detecting rare-variant associations in the noncoding genome is challenging. We present a scalable, flexible and streamlined rare-variant association analysis framework for biobank-scale whole-genome sequencing data, including gene-centric and non-gene-centric analyses by incorporating multiple variant functional annotations using various coding and noncoding units, conditional analysis, result summary and visualization.
A combination of light-sheet fluorescence microscopy (LSFM) with structured illumination doubles resolving power over LSFM alone. We show a practical implementation using a single objective for illumination and fluorescence detection and demonstrate its use for rapid volumetric imaging.
Light-Seq combines high resolution imaging with next generation sequencing of selected cell populations in fixed biological samples. Specifically, microscopically analyzed cells can be subjected to RNA expression profiling while keeping the sample intact for further assays, enabling cellular phenotypes and states to be assessed in the context of the original tissue.
RNA molecules designed by citizen scientists and probed in high-throughput experiments highlighted discrepancies among RNA folding algorithms in their ability to predict RNA structure ensembles. These datasets were used to train a new algorithm that demonstrated improved performance in a collection of independent datasets, including viral genomic RNAs and mRNAs probed in cells.
RNA comprises a substantial fraction of eukaryotic chromatin, but techniques to identify and map RNAs are cumbersome. We adapted existing tagmentation-based profiling techniques to enable chromatin-associated RNAs to be profiled in a simple workflow, enhancing the capability to identify regulatory RNAs.
BIONIC (Biological Network Integration using Convolutions) is a scalable deep learning network integration approach that learns and combines diverse data representations across a range of biological network types to consolidate knowledge of gene function. BIONIC outperforms existing integration approaches by capturing biological information more comprehensively and with greater accuracy than previously possible.
A genetically encoded green fluorescent sensor for oxytocin, MTRIAOT, offers an opportunity to perform real-time recording of brain oxytocin dynamics in living animals.
Joint profiling of multiple modalities in the same cell is challenging. We developed a method with a modular design to enable the simultaneous detection of chromatin accessibility and the transcriptome within single cells with flexible throughput.
A tissue engineering method using a 3D scaffolding enables the generation of an artificial human thymus from inducible pluripotent stem cells (iPSCs). The artificial thymus can be used to study human T cell development in hematopoietic humanized mice.
Scanning transmission electron microscopy (STEM) techniques reveal atomic-resolution details of organic and inorganic materials. The application of STEM to biological vitrified specimens under low-dose cryogenic imaging conditions demonstrates that STEM also achieves near-atomic-resolution 3D structures of biological macromolecules.