Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and major histocompatibility complex (MHC)-peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

Syndromic CLN3-Batten is a fatal, pediatric, neurodegenerative disease caused by variants in CLN3, which encodes the endolysosomal transmembrane CLN3 protein. No approved treatment for CLN3 is currently available. The protracted and asynchronous disease presentation complicates the evaluation of potential therapies using clinical disease progression parameters. Biomarkers as surrogates to measure the progression and effect of potential therapeutics are needed. We performed proteomic discovery studies using cerebrospinal fluid (CSF) samples from 28 CLN3-affected and 32 age-similar non-CLN3 individuals. Proximal extension assay (PEA) of 1467 proteins and untargeted data-dependent mass spectrometry [MS; MassIVE FTP server (ftp://MSV000090147@massive.ucsd.edu)] were used to generate orthogonal lists of protein marker candidates. At an adjusted p-value of <0.1 and threshold CLN3/non-CLN3 fold-change ratio of 1.5, PEA identified 54 and MS identified 233 candidate biomarkers. Some of these (NEFL, CHIT1) have been previously linked with other neurologic conditions. Others (CLPS, FAM217B, QRICH2, KRT16, ZNF333) appear to be novel. Both methods identified 25 candidate biomarkers, including CHIT1, NELL1, and ISLR2 which had absolute fold-change ratios >2. NELL1 and ISLR2 regulate axonal development in neurons and are intriguing new candidates for further investigation in CLN3. In addition to identifying candidate proteins for CLN3 research, this study provides a comparison of two large-scale proteomic discovery methods in CSF.

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node.js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node.js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node.js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode . This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy .

The ability to modulate cellular electrophysiology is a fundamental aspect for investigation of tissue development, maturation and function. Currently, there is a need for remote, non-genetic, light-induced control of cellular activity in native-like state, three-dimensional (3D) tissues such as spheroids and organoids. Here, we report a breakthrough hybrid-nanomaterial for remote, non-genetic photostimulation of both two-dimensional (2D) and 3D neural cellular systems. We combine one-dimensional (1D) nanowires (NWs) and 2D graphene flakes grown out-of-plane for highly controlled photostimulation at subcellular precision with laser energies lower than hundred nanojoules, 1-2 orders of magnitude lower than Au-, C- and Si-based photostimulation. Photostimulation using NW-templated 3D fuzzy graphene (NT-3DFG) is flexible due to its broadband absorption and does not generate cellular stress. Therefore, it serves a novel powerful toolset for studies of cell signaling within and between tissues and can enable new therapeutic interventions.

The ability to modulate cellular electrophysiology is fundamental to the investigation of development, function, and disease. Currently, there is a need for remote, nongenetic, light-induced control of cellular activity in two-dimensional (2D) and three-dimensional (3D) platforms. Here, we report a breakthrough hybrid nanomaterial for remote, nongenetic, photothermal stimulation of 2D and 3D neural cellular systems. We combine one-dimensional (1D) nanowires (NWs) and 2D graphene flakes grown out-of-plane for highly controlled photothermal stimulation at subcellular precision without the need for genetic modification, with laser energies lower than a hundred nanojoules, one to two orders of magnitude lower than Au-, C-, and Si-based nanomaterials. Photothermal stimulation using NW-templated 3D fuzzy graphene (NT-3DFG) is flexible due to its broadband absorption and does not generate cellular stress. Therefore, it serves as a powerful toolset for studies of cell signaling within and between tissues and can enable therapeutic interventions.

Investigating and modulating electrical activity of cardiomyocytes and neurons is essential to understanding their physiology, regeneration of excitable cells/tissues, and treatment of various disorders. Recently, microelectrode arrays (MEAs) have gained tremendous popularity since they enable multi-site recordings at sub-msec temporal resolution, provide two-way interaction with cells i.e. recording and stimulation, and have well-established microfabrication methods. However, currently used planar MEAs are not able to record and stimulate at sub-cellular resolution due to significant increase in electrode impedance which reduces the signal-to-noise ratio (SNR) of recorded signal. Currently there is a need to (i) enable high density electrode arrays, (ii) provide high spatial resolution, and (iii) prevent averaging of signals from multiple cells. To address these needs, we have developed three-dimensional out-of-plane grown fuzzy graphene (3DFG)-based MEA. Out-of-plane growth of single-to-few layers of graphene flakes leverages graphene's outstanding surface-to-volume ratio. The high surface area leads to significant reduction in impedance, thus enabling electrical recordings with high SNR and development of ultra-microelectrode arrays to perform sub-cellular measurements. Furthermore, the high flake density of graphene in 3DFG lead to high photo-absorbance in UV-NIR range. This opens up avenues to enable photo-thermal stimulation of cells with high precision and sub-cellular resolution. Our presented approach would greatly impact our basic understanding of signal transduction in complex cellular assemblies. Furthermore, it would provide a platform for developing and screening of therapeutics, and treatment of various cardiac and neurological diseases.

Background: Previous functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results. New method: The current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night. Results/comparison with existing method(s): Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles. Conclusions: It was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.

Characterizing the electrical activity of cardiomyocytes and neurons is crucial in understanding the complex processes in the heart and brain tissues, both in healthy and diseased states. Micro- and nanotechnologies have significantly improved the electrophysiological investigation of cellular networks. Carbon-based nanomaterials or nanocarbons, such as carbon nanotubes (CNTs), nanodiamonds (NDs) and graphene are promising building blocks for bioelectronics platforms owing to their outstanding chemical and physical properties. In this review, we discuss the various bioelectronics applications of nanocarbons and their derivatives. Furthermore, we touch upon the challenges that remain in the field and describe the emergence of carbon-based hybrid-nanomaterials that will potentially address those limitations, thus improving the capabilities to investigate the electrophysiology of excitable cells, both as a network and at the single cell level.

What do Google, Snapchat, Tinder, Amazon, and Uber have in common, besides soaring market share? They're platforms - a new business model that has quietly become the only game in town, creating vast fortunes for its founders while dominating everyone's daily life. A platform, by definition, creates value by facilitating an exchange between two or more interdependent groups. So, rather that making things, they simply connect people. The Internet today is awash in platforms - Facebook is responsible for nearly 25 percent of total Web visits, and the Google platform crash in 2013 took about 40 percent of Internet traffic with it. Representing the ten most trafficked sites in the U.S., platforms are also prominent over the globe; in China, they hold the top eight spots in web traffic rankings. The advent of mobile computing and its ubiquitous connectivity have forever altered how we interact with each other, melding the digital and physical worlds and blurring distinctions between offline and online. These platform giants are expanding their influence from the digital world to the whole economy. Yet, few people truly grasp the radical structural shifts of the last ten years. In Modern Monopolies, Alex Moazed and Nicholas L. Johnson tell the definitive story of what has changed, what it means for businesses today, and how managers, entrepreneurs, and business owners can adapt and thrive in this new era.

Although the basic morphological characteristics of neurons in the cerebellar cortex have been documented in several species, virtually nothing is known about the quantitative morphological characteristics of these neurons across different taxa. To that end, the present study investigated cerebellar neuronal morphology among eight different, large-brained mammalian species comprising a broad phylogenetic range: afrotherians (African elephant, Florida manatee), carnivores (Siberian tiger, clouded leopard), cetartiodactyls (humpback whale, giraffe) and primates (human, common chimpanzee). Specifically, several neuron types (e.g., stellate, basket, Lugaro, Golgi, and granule neurons; N = 317) of the cerebellar cortex were stained with a modified rapid Golgi technique and quantified on a computer-assisted microscopy system. There was a 64-fold variation in brain mass across species in our sample (from clouded leopard to the elephant) and a 103-fold variation in cerebellar volume. Most dendritic measures tended to increase with cerebellar volume. The cerebellar cortex in these species exhibited the trilaminate pattern common to all mammals. Morphologically, neuron types in the cerebellar cortex were generally consistent with those described in primates (Fox et al., 1967) and rodents (Palay and Chan-Palay 1974), although there was substantial quantitative variation across species. In particular, Lugaro neurons in the elephant appeared to be disproportionately larger than those in other species. To explore potential quantitative differences in dendritic measures across species, MARSplines analyses were used to evaluate whether species could be differentiated from each other based on dendritic characteristics alone. Results of these analyses indicated that there were significant differences among all species in dendritic measures.