Biology subjects

Cook, J. J.

Publications and source records attributed to Cook, J. J..

5 recordsLinked to original sources

bioRxiv2024

An Open Resource: MR and light sheet microscopy stereotaxic atlas of the mouse brain

We have combined MR histology and light sheet microscopy (LSM) of five postmortem C57BL/6J mouse brains in a stereotaxic space based on micro-CT yielding a multimodal 3D atlas with the highest spatial and contrast resolution yet reported. Brains were imaged in situ with multi gradient echo (mGRE) and diffusion tensor imaging (DTI) at 15 m resolution ([~] 2.4 million times that of clinical MRI). Scalar images derived from the average DTI and mGRE provide unprecedented contrast in 14 complementary 3D volumes, each highlighting distinct histologic features. The same tissues scanned with LSM and registered into the stereotaxic space provide 17 different molecular cell type stains. The common coordinate framework labels (CCFv3) complete the multimodal atlas. The atlas has been used to correct distortions in the Allen Brain Atlas and harmonize it with Franklin Paxinos. It provides a unique resource for stereotaxic labeling of mouse brain images from many sources.

neuroscience↗

bioRxiv2022

Restoring morphology of light sheet microscopy data based on magnetic resonance histology

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g. The Allen Brain Atlas (ABA), which limits the precision of measuring regional volumes and eliminates the individual difference in brain partitions for different strains, ages, and environmental exposures. Thus, these approaches obscure valuable anatomical information. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same brain restoring the morphology of the LSM images to the in-skull morphology. Our pipeline which maps 3D LSM data (terabyte level per dataset) to magnetic resonance histology of the same mouse brain provides accurate registration with low displacement error in 10 hours with limited manual input. Optimization and validation, from initialization of the data, designing the quantitative loss function, optimizing the 20+ image processing variables on multiple resolution scales, and finalizing the application on full resolution data has been integrated through a structured workflow. Excellent agreement has been seen between registered data and reference data both locally and globally. This workflow has been applied to a collection of datasets with varied MRH and LSM contrast providing a routine method for streamlined registration of LSM images to MRH.

bioinformatics↗

bioRxiv2022

Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain

Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 m isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 m isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000 s/mm2 to 8000 s/mm2), angular resolution (10 to 126), and spatial resolution (25 {micro}m to 200 {micro}m). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles are necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R2 > 0.99); a single-shell acquisition with b value of 3000 s/mm2 shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.

neuroscience↗

bioRxiv2020

A time-course study of actively stained mouse brains: DTI parameter and connectomic stability over one year

While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed ex-vivo tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy across twelve representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first two weeks after fixation. The same parameters were stable over a two to eight-week window after fixation which means confounds from tissue stability over that scanning window are minimal. Quantitative connectomes were analyzed over the same time period with extension out to one year. While there is some change in the scalar metrics at one year after fixation, these changes are sufficiently small, particularly in white matter to support reproducible connectomes over a period ranging from two weeks to one year post fixation. These findings delineate a stable scanning period during which brain volumes, diffusion scalar metrics and connectomes are remarkably stable.

neuroscience↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗