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The entorhinal cortex (EC) is a highly-interconnected hub for multisensory integration and memory processing1–3, containing diverse neuronal subtypes4,5 including subpopulations that are uniquely spatially-tuned6,7. Although many spatial and memory functions develop in infancy, it is considered that neurogenesis and neuronal migration to the EC occurs prenatally. Here we show that the postnatal human temporal lobe contains a prominent stream with large chains of young migrating neurons and many individual neurons breaking away directed into the EC. The EC stream forms between the second and third trimesters of prenatal development when the lateral ventricle walls in the temporal lobe collapse, displacing the subventricular zone (SVZ) and dividing radial glia. At birth, the EC stream follows a path of radial glial fibers in the site of the collapsed ventricle. Migratory chains persist up to 11 months postnatally; however, many individually migrating young neurons can still be detected in the EC at 2 years of age and a few isolated cells at 3 years of age. Within the EC at birth, immature neurons are a mixed population expressing markers of the medial ganglionic eminence (MGE) and caudal ganglionic eminence (CGE), but postnatally rapidly become primarily CGE-derived. Using single-nuclei RNAseq we identified these lineages and found that the MGE-derived neurons matured at earlier postnatal ages compared to those derived from the CGE. The CGE interneurons arriving and maturing the latest included subtypes expressing calretinin (CR), reelin (RELN), and vasoactive intestinal protein (VIP) many of which settle in layer II of the entorhinal cortex. This study reveals that the human EC is still being constructed during the first years of life revealing the largest known postnatal stream of migratory neurons in humans. The protracted postnatal arrival of a diverse population of interneurons could contribute to plasticity8,9 and proper excitation-inhibition balance10,11 within these highly connected brain circuits.

Fish embryonic hatching glands (HGs) secrete choriolysin-zymogens, which when activated degrade the chorion, allowing larval exit. Thus, hatching encompasses two dissimilar choriolysin-processes: pre-choriolysis including activated choriolysins accessing the perivitelline space (PVS), followed by choriolysis. Discovery of serine-proteolytic zonase in Atlantic salmon hatching fluid (HF) raises questions concerning its participation in hatching. This work aims to identify salmon choriolysins, and evaluate their role and that of zonase during hatching. Precocious salmon hatching occurs under alkaline conditions, producing an HF containing similar metallo- and serine- proteolytic activities. Purified zonase is selectively inhibited by peFabloc, whose MW (580 Da) allows diffusion through the chorion into the PVS. Without apparent toxicity, brief peFabloc-pretreatment (2 mM) of salmon eggs reduced precocious hatching substantially, compatible with a zonase-relevance for hatching. Atlantic salmon differed from other fishes since their HGs were not immuno-stained by polyclonal antibodies against pike choriolysins. However, cloning and sequencing experiments revealed a single low choriolytic enzyme (LCE) of 69% identity to pike LCE. Similar experiments with high choriolytic enzymes (HCEs) revealed two types (HCE-1 and HCE-2) with respectively 71% and 91% identity to pike HCE-1HCE-2. In situ hybridization revealed typical HGs. However, zebrafish-choriolysis is achieved by HCE-class choriolysins alone. Another zebrafish choriolysin (HE2) was not expressed. Peptide-bond hydrolysis by non-choriolytic zonase mimicks HCE-action generating hydrophilic sites for LCE-choriolytic catalysis. Ultimately, precise definitions of choriolytic and pre-choriolytic catalysis requires developmental genetics. Our data are compatible with a complex pre-choriolytic hatching-stage in Atlantic salmon, before LCE-choriolysis degrades the chorion.

Elevated blood ammonia (hyperammonemia) may cause delirium, brain damage, and even death. Effective treatments exist, but preventing permanent neurological sequelae requires rapid, accurate, and serial measurements of blood ammonia. Standard methods require volumes of 1 to 3 mL, centrifugation to isolate plasma, and a turn-around time of 2 h. Collection, handling, and processing requirements mean that community clinics, particularly those in low resource settings, cannot provide reliable measurements. We describe a method to measure ammonia from small-volume whole blood samples in 2 min. The method alkalizes blood to release gas-phase ammonia for detection by a fuel cell. When an inexpensive first-generation instrument designed for 100 μL of blood was tested on adults and children in a clinical study, the method showed a strong correlation (R2 = 0.97) with an academic clinical laboratory for plasma ammonia concentrations up to 500 μM (16 times higher than the upper limit of normal). A second-generation hand-held instrument designed for 10-20 μL of blood showed a near-perfect correlation (R2 = 0.99) with healthy donor blood samples containing known amounts of added ammonium chloride up to 1000 μM. Our method can enable rapid and inexpensive measurement of blood ammonia, transforming diagnosis and management of hyperammonemia.

The relationship of genotypes to phenotypes can be modified by environmental inputs. Such crucial environmental inputs include metabolic cues derived from microbes living together with animals. Thus the analysis of genetic effects on animals’ physiology can be confounded by variations in the metabolic profile of microbes. Caenorhabditis elegans exposed to distinct bacterial strains and species exhibit phenotypes different at cellular, developmental and behavioral levels. Here we reported metabolomic profiles of three Escherichia coli strains, B strain OP50, K-12 strain MG1655, and B-K-12 hybrid strain HB101, and also different mitochondrial and fat storage phenotypes of C. elegans exposed to MG1655 and HB101 versus OP50. We found that these metabolic phenotypes of C. elegans are not correlated with overall metabolic patterning of bacterial strains, but their specific metabolites. In particular, the fat storage phenotype is traced to the betaine level in different bacterial strains. HT115 is another K-12 E. coli strain that is commonly utilized to elicit an RNA interference response, and we showed that C. elegans exposed to OP50 and HT115 exhibit differences in mitochondrial morphology and fat storage levels. We thus generated an RNA interference competent OP50 (iOP50) strain that can robustly and consistently knockdown endogenous C. elegans genes in different tissues. Together, these studies suggest the importance of specific bacterial metabolites in regulating the host’s physiology, and provide a tool to prevent confounding effects when analyzing genotype-phenotype interactions under different bacterial backgrounds.

This paper identifies and explores the challenges of teaching specialist camera and studio craft based production courses online given the Covid-19 pandemic. The Covid-19 pandemic resulted in all courses at the National Centre for Computer Animation, Bournemouth University being delivered online rather than face-to-face. This was particularly challenging for the units that involved camera production. This paper will explore the different strategies used to deliver online three different camera based units with very different intended learning outcomes; a) Visual Storytelling and Previsualisation, b) Visual Studies, and, c) Visual Effects Photography and Acquisition. A novel teaching model emerged that allowed for enhanced student engagement and practice whilst working within the constraints of inaccessibility to specialist camera equipment and location work. This can be summed up as an iterative spiral model; Introduce>Set>Practice>Reinforce>Assess>Feedback The resulting outcomes, both in terms of the teaching and delivery approaches as well as the student work will be discussed and analysed.

The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

Additional file 1: Figure S1. Analysis of SCMK classification bias on PBMC scRNA-seq datasets (e.g., 7-donor PBMC, resting/stimulated PBMC) generated without LMOs. Figure S2. Analysis of gene expression signatures associated with Trima apheresis. Figure S3. Analysis of PBMC cell type proportions across different experimental conditions. Figure S4. Analysis of PBMC cell type proportions and gene expression states linked to alloreactivity in Zheng et al. scRNA-seq dataset. Figure S5. scRNA-seq quality-control and cell type annotation workflow, 8-donor PBMC dataset. Figure S6. scRNA-seq quality-control and cell type annotation workflow, Zheng et al. PBMC dataset. Figure S7. CD4+ T-cell subset classification workflow for 8-donor, 7-donor, and Zheng et al. scRNA-seq datasets. Figure S8. PBMC cell type annotation workflow, 7-donor PBMC and resting/stimulated PBMC datasets.