Tau differentially regulates the transport of early endosomes and lysosomes.

Microtubule-associated proteins (MAPs) modulate the motility of kinesin and dynein along microtubules to control the transport of vesicles and organelles. The neuronal MAP tau inhibits kinesin-dependent transport. Phosphorylation of tau at Tyr-18 by fyn kinase results in weakened inhibition of kinesin-1. We examined the motility of early endosomes and lysosomes in cells expressing wild-type (WT) tau and phosphomimetic Y18E tau. We quantified the effects on motility as a function of the tau expression level. Lysosome motility is strongly inhibited by tau. Y18E tau preferentially inhibits lysosomes in the cell periphery, while centrally located lysosomes are less affected. Early endosomes are more sensitive to tau than lysosomes and are inhibited by both WT and Y18E tau. Our results show that different cargoes have disparate responses to tau, likely governed by the types of kinesin motors driving their transport. In support of this model, kinesin-1 and -3 are strongly inhibited by tau while kinesin-2 and dynein are less affected. In contrast to kinesin-1, we find that kinesin-3 is strongly inhibited by phosphorylated tau.

Benchmarking Single-Cell mRNA-Sequencing Technologies Uncovers Differences in Sensitivity and Reproducibility in Cell Types With Low RNA Content.

Single-cell RNA sequencing (scRNA-seq) has the ability to classify each cell and determine the transcriptomic profile of specific cell types and cells of a given disease state; however, sensitivity of the gene count for each cell can be a critical component to the success of a single-cell study. The recently introduced SMART-Seq Single Cell PLUS Kit (SSsc PLUS) claims to provide higher sensitivity and reproducibility versus popular methods for the sequencing analysis of single cells. Here, the cDNA-generation component of the kit, SMART-Seq Single Cell Kit (SSsc), was compared with the popular homebrew protocol, Smart-seq2, and its update, Smart-seq3. The SMART-Seq Library Prep Kit from SSsc PLUS was benchmarked against a commonly used scRNA-seq library preparation method, Illumina Nextera XT. Finally, the SSsc chemistry was tested in both full and fractional volumes on 2 popular liquid-handler devices to investigate whether the high sensitivity was maintained in miniaturization. We demonstrate that SSsc PLUS outperforms these other full-length methods in convenience, sensitivity, gene identification, and reproducibility while also offering full compatibility with automation platforms.

RAGE Isoforms, its Ligands and their Role in Pathophysiology of Alzheimer's Disease.

Receptor for Advanced Glycation End product (RAGE) plays a crucial role in a variety of physiological and pathological processes due to its ability to bind a broad repertory of ligands. There are also multiple forms of RAGE that exist; some work on promoting feed-forward pathways while others perform inhibitory actions. This review focuses on the RAGE isoforms expression, its intracellular pathways activation via RAGE- ligand interaction, and its importance in the physiological and pathological process of the brain. Many studies have suggested that RAGE induces the pathophysiological changes in Alzheimer's disease (AD) by being an intermediator of inflammation and inducer of oxidative stress. The critical roles played by RAGE in AD include its involvement in amyloid-beta (Aß) production, clearance, synaptic impairment, and neuronal circuit dysfunction. RAGE-Aß interaction also mediates the bi-directional crosstalk between peripheral and central systems. This interaction underlies a potential molecular pathway that disrupts the material structure and physiology of the brain. This review highlights the structure-function relation for RAGEAß interaction and the role of RAGE as a potential target in the development of treatments for AD.

Massively parallel nanowell-based single-cell gene expression profiling.

BACKGROUND: Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. RESULTS: Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture ~1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells. CONCLUSIONS: Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.

Enhanced prediction of conformational flexibility and phosphorylation in proteins.

Many sequence-based predictors of structural and functional properties of proteins have been developed in the past. In this study, we developed new methods for predicting measures of conformational flexibility in proteins, including X-ray structure-derived temperature (B-) factors and the variance within NMR structural ensemble, as effectively measured by the solvent accessibility standard deviations (SASDs). We further tested whether these predicted measures of conformational flexibility in crystal lattices and solution, respectively, can be used to improve the prediction of phosphorylation in proteins. The latter is an example of a common post-translational modification that modulates protein function, e.g., by affecting interactions and conformational flexibility of phosphorylated sites. Using robust epsilon-insensitive support vector regression (ε-SVR) models, we assessed two specific representations of protein sequences: one based on the position-specific scoring matrices (PSSMs) derived from multiple sequence alignments, and an augmented representation that incorporates real-valued solvent accessibility and secondary structure predictions (RSA/SS) as additional measures of local structural propensities. We showed that a combination of PSSMs and real-valued SS/RSA predictions provides systematic improvements in the accuracy of both B-factors and SASD prediction. These intermediate predictions were subsequently combined into an enhanced predictor of phosphorylation that was shown to significantly outperform methods based on PSSM alone. We would like to stress that to the best of our knowledge, this is the first example of using predicted from sequence NMR structure-based measures of conformational flexibility in solution for the prediction of other properties of proteins. Phosphorylation prediction methods typically employ a two-class classification approach with the limitation that the set of negative examples used for training may include some sites that are simply unknown to be phosphorylated. While one-class classification techniques have been considered in the past as a solution to this problem, their performance has not been systematically compared to two-class techniques. In this study, we developed and compared one- and two-class support vector machine (SVM)-based predictors for several commonly used sets of attributes. [These predictors are being made available at http://sable.cchmc.org/].

Artificial intelligence approaches for rational drug design and discovery.

Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.