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Bioluminescence imaging is a tremendous asset to medical research, providing a way to monitor living cells noninvasively within their natural environments. Advances in imaging methods allow researchers to measure tumor growth, visualize developmental processes, and track cell-cell interactions. Yet technical limitations exist, and it is difficult to image deep tissues or detect low cell numbers in vivo. Iwano et al. designed a bioluminescence imaging system that produces brighter emission by up to a factor of 1000 compared with conventional technology (see the Perspective by Nasu and Campbell). Individual tumor cells were successfully visualized in the lungs of mice. Small numbers of striatal neurons were detected in the brains of naturally behaving marmosets. The ability of the substrate to cross the blood-brain barrier should provide important opportunities for neuroscience research. Science , this issue p. 935 ; see also p. 868 A bioengineered light source allows in vivo imaging of individual cells. Bioluminescence is a natural light source based on luciferase catalysis of its substrate luciferin. We performed directed evolution on firefly luciferase using a red-shifted and highly deliverable luciferin analog to establish AkaBLI, an all-engineered bioluminescence in vivo imaging system. AkaBLI produced emissions in vivo that were brighter by a factor of 100 to 1000 than conventional systems, allowing noninvasive visualization of single cells deep inside freely moving animals. Single tumorigenic cells trapped in the mouse lung vasculature could be visualized. In the mouse brain, genetic labeling with neural activity sensors allowed tracking of small clusters of hippocampal neurons activated by novel environments. In a marmoset, we recorded video-rate bioluminescence from neurons in the striatum, a deep brain area, for more than 1 year. AkaBLI is therefore a bioengineered light source to spur unprecedented scientific, medical, and industrial applications.

The failure of neuroprotective treatment-related clinical trials may be partially caused by unestablished animal models. Existing animal models are less likely to provide occlusion confined to the middle cerebral artery (MCA), making transarterial intervention difficult. We aimed to develop a novel focal stroke model using a microcatheter and zirconium dioxide that is non-magnetic under fluoroscopic guidance, which can monitor MCA occlusion and can improve hemorrhagic complications. Using male Sprague Dawley rats (n = 10), a microcatheter was navigated from the caudal ventral artery to the left internal carotid artery using an X-ray fluoroscopy to establish local occlusion. All rat cerebral angiographies were successful. No rats had hemorrhagic complications. Eight (80%) rats underwent occlusion of the MCA bifurcation by zirconium dioxide. Accidentally, the left posterior cerebral artery was failure embolized in 2 rats (20%). The median operating time was 8 min. All rats of occlusion MCA revealed an incomplete hemiparesis on the right side with neurological deficit score ranging from 1 to 3 (median 1, interquartile range 1–3) at 24 h after the induction of ischemia. Moreover, 2% 2,3,5-triphenyl tetrazolium chloride staining showed that the median infarct volume (mm 3 ) was 280 (interquartile range 267–333) 24 h after the left MCA bifurcation occlusion. We present a novel rat model for focal stroke using a microcatheter and zirconium dioxide which does not affect the MRI. The model is predictable which is well confined within the territory supplied by the MCA, and reproducibility of this model is 80%. Fluoroscopy was able to identify which the MCA occlusion and model success while creating the model. It permitted exclusion of animals with complications from the experiment.

White adipose tissue (WAT) in mice undergoes browning in response to cold exposure. Brown and beige adipocytes contain multilocular lipid droplets and abundant iron-containing mitochondria expressing uncoupling protein 1 (UCP-1). Cold exposure-induced browning WAT is accompanied by increased density of blood vessels and sympathetic nerve fibres. A previous study reported a more than threefold increase in sympathetic nerve dendritic tone in inguinal white adipose tissue (iWAT) after cold exposure. Therefore, we hypothesized that water molecule diffusion would be more restricted in brown and beige adipocytes compared to white adipocytes. The characteristics of T2* values and anisotropy parameters by diffusion tensor imaging (DTI) in browning WAT are unclear. The aim of the present study was to investigate the effect of cold exposure on T2* values and anisotropy parameters (fractional anisotropy [FA], apparent diffusion coefficient [ADC], radial diffusivity [RD] and eigenvalues λ1, λ2, λ3) in brown adipose tissue (BAT), iWAT and epididymal white adipose tissue (epiWAT). Furthermore, these parameters were investigated in vivo through additional validation experiments in three control mice. Mice in the cold exposure (CE) group were exposed to a cold environment at 4 °C for 10 days, while these in the control (C) group were maintained at 22 °C throughout the experiment. T2* values, FA, ADC, RD and eigenvalues (λ1, λ2, λ3) were measured in BAT, iWAT and epiWAT using a 9.4T magnetic resonance scanner (Bruker Biospin AG). T2* values of epiWAT in the C group were significantly higher than these of BAT in the C group and iWAT in the CE group. No significant differences were observed between groups for FA, ADC, RD, λ1 and λ2 of iWAT and epiWAT. However, the λ3 values of iWAT and epiWAT in the CE group were significantly higher than these of iWAT, epiWAT and BAT in the C group. Compared to ex vivo measurements, in vivo measurements in control mice showed higher T2* values with reduced intertissue variability while maintaining tissue-specific patterns. These results suggest that T2* values and anisotropy parameters might serve as potential markers for the assessment of adipose tissue plasticity. Further studies are required to investigate their utility as non-invasive indicators of browning WAT.

Skeletal muscles include fast and slow muscle fibers. The tibialis anterior muscle (TA) is mainly composed of fast muscle fibers, whereas the soleus muscle (SOL) is mainly composed of slow muscle fibers. However, a noninvasive approach for appropriately investigating the characteristics of muscles is not available. Monitoring of skeletal muscle characteristics can help in the evaluation of the effects of strength training and diseases on skeletal muscles. The present study aimed to determine whether q-space imaging can distinguish between TA and SOL in in vivo mice. In vivo magnetic resonance imaging of the right calves of mice (n = 8) was performed using a 7-Tesla magnetic resonance imaging system with a cryogenic probe. TA and SOL were assessed. q-space imaging was performed with a field of view of 10 mm × 10 mm, matrix of 48 × 48, and section thickness of 1000 μm. There were ten b-values ranging from 0 to 4244 s/mm2, and each b-value had diffusion encoding in three directions. Magnetic resonance imaging findings were compared with immunohistological findings. Full width at half maximum and Kurtosis maps of q-space imaging showed signal intensities consistent with immunohistological findings for both fast (myosin heavy chain II) and slow (myosin heavy chain I) muscle fibers. With regard to quantification, both full width at half maximum and Kurtosis could represent the immunohistological findings that the cell diameter of TA was larger than that of SOL (P < 0.01). q-space imaging could clearly differentiate TA from SOL using differences in cell diameters. This technique is a promising method to noninvasively estimate the fiber type ratio in skeletal muscles, and it can be further developed as an indicator of muscle characteristics.

•We developed a Group Surrogate Data Generating Model (GSDGM) to generate the group centroid of multivariate time-series.•We developed a similarity quantification method called Multivariate Time-series Ensemble Similarity Score (MTESS).•GSDGM and MTESS can be used for fingerprint analysis in human rs-fMRI data and distinguishes outlier sessions.•We provide a GSDGM and MTESS Toolbox that can be freely downloaded from https://github.com/takuto-okuno-riken/mtess. Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.

Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. Here we present an integrated neuro-histological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common marmoset (Callithrix jacchus). Individual brains are sectioned into ~1700 20 µm sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labeled sections. Systematic in-vivo MRI of the individual animals facilitates injection placement into reference-atlas defined anatomical compartments. Further, by combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to accurately map individual brains into a common reference atlas despite the significant individual variation. This approach will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain.

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

Currently, we can label the certain cells by transducing specific genes, called reporter genes, and distinguish them from other cells. For example, fluorescent protein such as green fluorescence protein (GFP) is commonly used for cell labeling. However, fluorescent protein is difficult to observe in living animals. We can observe the reporter signals of the luciferin-luciferase system from the outside of living animals using in vivo imaging systems, although the resolution of this system is low. Therefore, in this study, we examined the reporter genes, which allowed the MRI-mediated observation of labeled cells in living animals. As a preliminary stage of animal study, we transduced some groups of plasmids that coded the protein that could take and store metal ions to the cell culture, added metal ions solutions, and measured their T1 or T2 relaxation values. Finally, we specified the best reporter gene combination for MRI, which was the combination of transferrin receptor, DMT1, and Ferritin-M6A for T1WI, and Ferritin-M6A for T2WI.

•We employed a sufficient sample size to overcome inter-individual variability and noise.•We showed the structural and functional brain connectivity within the marmoset species.•We demonstrated a significantly high similarity in structural neural connections.•We identified 7 networks under the commonly used isoflurane inhalation anesthesia.•We were able to delineate the features of isoflurane anesthesia in common marmosets. The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.