The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism.

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson's Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.

Automated four-dimensional long term imaging enables single cell tracking within organotypic brain slices to study neurodevelopment and degeneration.

Current approaches for dynamic profiling of single cells rely on dissociated cultures, which lack important biological features existing in tissues. Organotypic slice cultures preserve aspects of structural and synaptic organisation within the brain and are amenable to microscopy, but established techniques are not well adapted for high throughput or longitudinal single cell analysis. Here we developed a custom-built, automated confocal imaging platform, with improved organotypic slice culture and maintenance. The approach enables fully automated image acquisition and four-dimensional tracking of morphological changes within individual cells in organotypic cultures from rodent and human primary tissues for at least 3 weeks. To validate this system, we analysed neurons expressing a disease-associated version of huntingtin (HTT586Q138-EGFP), and observed that they displayed hallmarks of Huntington's disease and died sooner than controls. By facilitating longitudinal single-cell analyses of neuronal physiology, our system bridges scales necessary to attain statistical power to detect developmental and disease phenotypes.

Assessing microscope image focus quality with deep learning.

BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. RESULTS: We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. CONCLUSIONS: Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Exceedingly small iron oxide nanoparticles as positive MRI contrast agents.

Medical imaging is routine in the diagnosis and staging of a wide range of medical conditions. In particular, magnetic resonance imaging (MRI) is critical for visualizing soft tissue and organs, with over 60 million MRI procedures performed each year worldwide. About one-third of these procedures are contrast-enhanced MRI, and gadolinium-based contrast agents (GBCAs) are the mainstream MRI contrast agents used in the clinic. GBCAs have shown efficacy and are safe to use with most patients; however, some GBCAs have a small risk of adverse effects, including nephrogenic systemic fibrosis (NSF), the untreatable condition recently linked to gadolinium (Gd) exposure during MRI with contrast. In addition, Gd deposition in the human brain has been reported following contrast, and this is now under investigation by the US Food and Drug Administration (FDA). To address a perceived need for a Gd-free contrast agent with pharmacokinetic and imaging properties comparable to GBCAs, we have designed and developed zwitterion-coated exceedingly small superparamagnetic iron oxide nanoparticles (ZES-SPIONs) consisting of ∼3-nm inorganic cores and ∼1-nm ultrathin hydrophilic shell. These ZES-SPIONs are free of Gd and show a high T1 contrast power. We demonstrate the potential of ZES-SPIONs in preclinical MRI and magnetic resonance angiography.

Molecular imaging with engineered physiology.

In vivo imaging techniques are powerful tools for evaluating biological systems. Relating image signals to precise molecular phenomena can be challenging, however, due to limitations of the existing optical, magnetic and radioactive imaging probe mechanisms. Here we demonstrate a concept for molecular imaging which bypasses the need for conventional imaging agents by perturbing the endogenous multimodal contrast provided by the vasculature. Variants of the calcitonin gene-related peptide artificially activate vasodilation pathways in rat brain and induce contrast changes that are readily measured by optical and magnetic resonance imaging. CGRP-based agents induce effects at nanomolar concentrations in deep tissue and can be engineered into switchable analyte-dependent forms and genetically encoded reporters suitable for molecular imaging or cell tracking. Such artificially engineered physiological changes, therefore, provide a highly versatile means for sensitive analysis of molecular events in living organisms.

Cell Type-Specific Circuit Mapping Reveals the Presynaptic Connectivity of Developing Cortical Circuits.

The mammalian cerebral cortex is a dense network composed of local, subcortical, and intercortical synaptic connections. As a result, mapping cell type-specific neuronal connectivity in the cerebral cortex in vivo has long been a challenge for neurobiologists. In particular, the development of excitatory and inhibitory interneuron presynaptic input has been hard to capture. We set out to analyze the development of this connectivity in the first postnatal month using a murine model. First, we surveyed the connectivity of one of the earliest populations of neurons in the brain, the Cajal-Retzius (CR) cells in the neocortex, which are known to be critical for cortical layer formation and are hypothesized to be important in the establishment of early cortical networks. We found that CR cells receive inputs from deeper-layer excitatory neurons and inhibitory interneurons in the first postnatal week. We also found that both excitatory pyramidal neurons and inhibitory interneurons received broad inputs in the first postnatal week, including inputs from CR cells. Expanding our analysis into the more mature brain, we assessed the inputs onto inhibitory interneurons and excitatory projection neurons, labeling neuronal progenitors with Cre drivers to study discrete populations of neurons in older cortex, and found that excitatory cortical and subcortical inputs are refined by the fourth week of development, whereas local inhibitory inputs increase during this postnatal period. Cell type-specific circuit mapping is specific, reliable, and effective, and can be used on molecularly defined subtypes to determine connectivity in the cortex. SIGNIFICANCE STATEMENT: Mapping cortical connectivity in the developing mammalian brain has been an intractable problem, in part because it has not been possible to analyze connectivity with cell subtype precision. Our study systematically targets the presynaptic connections of discrete neuronal subtypes in both the mature and developing cerebral cortex. We analyzed the connections that Cajal-Retzius cells make and receive, and found that these cells receive inputs from deeper-layer excitatory neurons and inhibitory interneurons in the first postnatal week. We assessed the inputs onto inhibitory interneurons and excitatory projection neurons, the major two types of neurons in the cortex, and found that excitatory inputs are refined by the fourth week of development, whereas local inhibitory inputs increase during this postnatal period.

Magneto-fluorescent core-shell supernanoparticles.

Magneto-fluorescent particles have been recognized as an emerging class of materials that exhibit great potential in advanced applications. However, synthesizing such magneto-fluorescent nanomaterials that simultaneously exhibit uniform and tunable sizes, high magnetic content loading, maximized fluorophore coverage at the surface and a versatile surface functionality has proven challenging. Here we report a simple approach for co-assembling magnetic nanoparticles with fluorescent quantum dots to form colloidal magneto-fluorescent supernanoparticles. Importantly, these supernanoparticles exhibit a superstructure consisting of a close-packed magnetic nanoparticle 'core', which is fully surrounded by a 'shell' of fluorescent quantum dots. A thin layer of silica coating provides high colloidal stability and biocompatibility, and a versatile surface functionality. We demonstrate that after surface pegylation, these silica-coated magneto-fluorescent supernanoparticles can be magnetically manipulated inside living cells while being optically tracked. Moreover, our silica-coated magneto-fluorescent supernanoparticles can also serve as an in vivo multi-photon and magnetic resonance dual-modal imaging probe.

Screen-based analysis of magnetic nanoparticle libraries formed using peptidic iron oxide ligands.

The identification of effective polypeptide ligands for magnetic iron oxide nanoparticles (IONPs) could considerably accelerate the high-throughput analysis of IONP-based reagents for imaging and cell labeling. We developed a procedure for screening IONP ligands and applied it to compare candidate peptides that incorporated carboxylic acid side chains, catechols, and sequences derived from phage display selection. We found that only l-3,4-dihydroxyphenylalanine (DOPA)-containing peptides were sufficient to maintain particles in solution. We used a DOPA-containing sequence motif as the starting point for generation of a further library of over 30 peptides, each of which was complexed with IONPs and evaluated for colloidal stability and magnetic resonance imaging (MRI) contrast properties. Optimal properties were conferred by sequences within a narrow range of biophysical parameters, suggesting that these sequences could serve as generalizable anchors for formation of polypeptide-IONP complexes. Differences in the amino acid sequence affected T1- and T2-weighted MRI contrast without substantially altering particle size, indicating that the microstructure of peptide-based IONP coatings exerts a substantial influence and could be manipulated to tune properties of targeted or responsive contrast agents. A representative peptide-IONP complex displayed stability in biological buffer and induced persistent MRI contrast in mice, indicating suitability of these species for in vivo molecular imaging applications.