Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years.

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1. We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2, selected using World Health Organization recommendations for growth standards3. Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5. The atlas was produced using 1,059 optimal quality, three-dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6-8. The atlas corresponds structurally to published magnetic resonance images9, but with finer anatomical details in deep grey matter. The between-study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks' gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks' gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment.

BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography.

Brain extraction (masking of extra-cerebral tissues) and alignment are fundamental first steps of most neuroimage analysis pipelines. The lack of automated solutions for 3D ultrasound (US) has therefore limited its potential as a neuroimaging modality for studying fetal brain development using routinely acquired scans. In this work, we propose a convolutional neural network (CNN) that accurately and consistently aligns and extracts the fetal brain from minimally pre-processed 3D US scans. Our multi-task CNN, Brain Extraction and Alignment Network (BEAN), consists of two independent branches: 1) a fully-convolutional encoder-decoder branch for brain extraction of unaligned scans, and 2) a two-step regression-based branch for similarity alignment of the brain to a common coordinate space. BEAN was tested on 356 fetal head 3D scans spanning the gestational range of 14 to 30 weeks, significantly outperforming all current alternatives for fetal brain extraction and alignment. BEAN achieved state-of-the-art performance for both tasks, with a mean Dice Similarity Coefficient (DSC) of 0.94 for the brain extraction masks, and a mean DSC of 0.93 for the alignment of the target brain masks. The presented experimental results show that brain structures such as the thalamus, choroid plexus, cavum septum pellucidum, and Sylvian fissure, are consistently aligned throughout the dataset and remain clearly visible when the scans are averaged together. The BEAN implementation and related code can be found under www.github.com/felipemoser/kelluwen.

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

Cryo-SOFI enabling low-dose super-resolution correlative light and electron cryo-microscopy.

Correlative light and electron cryo-microscopy (cryo-CLEM) combines information from the specific labeling of fluorescence cryo-microscopy (cryo-FM) with the high resolution in environmental context of electron cryo-microscopy (cryo-EM). Exploiting super-resolution methods for cryo-FM is advantageous, as it enables the identification of rare events within the environmental background of cryo-EM at a sensitivity and resolution beyond that of conventional methods. However, due to the need for relatively high laser intensities, current super-resolution cryo-CLEM methods require cryo-protectants or support films which can severely reduce image quality in cryo-EM and are not compatible with many samples, such as mammalian cells. Here, we introduce cryogenic super-resolution optical fluctuation imaging (cryo-SOFI), a low-dose super-resolution imaging scheme based on the SOFI principle. As cryo-SOFI does not require special sample preparation, it is fully compatible with conventional cryo-EM specimens, and importantly, it does not affect the quality of cryo-EM imaging. By applying cryo-SOFI to a variety of biological application examples, we demonstrate resolutions up to ∼135 nm, an improvement of up to three times compared with conventional cryo-FM, while maintaining the specimen in a vitrified state for subsequent cryo-EM. Cryo-SOFI presents a general solution to the problem of specimen devitrification in super-resolution cryo-CLEM. It does not require a complex optical setup and can easily be implemented in any existing cryo-FM system.

Structure of the Lassa virus glycan shield provides a model for immunological resistance.

Lassa virus is an Old World arenavirus endemic to West Africa that causes severe hemorrhagic fever. Vaccine development has focused on the envelope glycoprotein complex (GPC) that extends from the virion envelope. The often inadequate antibody immune response elicited by both vaccine and natural infection has been, in part, attributed to the abundance of N-linked glycosylation on the GPC. Here, using a virus-like-particle system that presents Lassa virus GPC in a native-like context, we determine the composite population of each of the N-linked glycosylation sites presented on the trimeric GPC spike. Our analysis reveals the presence of underprocessed oligomannose-type glycans, which form punctuated clusters that obscure the proteinous surface of both the GP1 attachment and GP2 fusion glycoprotein subunits of the Lassa virus GPC. These oligomannose clusters are seemingly derived as a result of sterically reduced accessibility to glycan processing enzymes, and limited amino acid diversification around these sites supports their role protecting against the humoral immune response. Combined, our data provide a structure-based blueprint for understanding how glycans render the glycoprotein spikes of Lassa virus and other Old World arenaviruses immunologically resistant targets.

Cellular Uptake of Gold Nanoparticles and Their Behavior as Labels for Localization Microscopy.

Gold nanoparticles (GNPs) enhance the damaging absorbance effects of high-energy photons in radiation therapy by increasing the emission of Auger-photoelectrons in the nm-µm range. It has been shown that the incorporation of GNPs has a significant effect on radiosensitivity of cells and their dose-dependent clonogenic survival. One major characteristic of GNPs is also their diameter-dependent cellular uptake and retention. In this article, we show by means of an established embodiment of localization microscopy, spectral position determination microscopy (SPDM), that imaging with nanometer resolution and systematic counting of GNPs becomes feasible, because optical absorption and plasmon resonance effects result in optical blinking of GNPs at a size-dependent wavelength. To quantify cellular uptake and retention or release, SPDM with GNPs that have diameters of 10 and 25 nm was performed after 2 h and after 18 h. The uptake of the GNPs in HeLa cells was either achieved via incubation or transfection via DNA labeling. On average, the uptake by incubation after 2 h was approximately double for 10 nm GNPs as compared to 25 nm GNPs. In contrast, the uptake of 25 nm GNPs by transfection was approximately four times higher after 2 h. The spectral characteristics of the fluorescence of the GNPs seem to be environment-dependent. In contrast to fluorescent dyes that show blinking characteristics due to reversible photobleaching, the blinking of GNPs seems to be stable for long periods of time, and this facilitates their use as an appropriate dye analog for SPDM imaging.