Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge.

Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).

DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing.

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

Preliminary In Vitro and In Vivo Insights of In Silico Candidate Repurposed Drugs for Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and is the most common type of dementia. Although a considerably large amount of money has been invested in drug development for AD, no disease modifying treatment has been detected so far. In our previous work, we developed a computational method to highlight stage-specific candidate repurposed drugs against AD. In this study, we tested the effect of the top 13 candidate repurposed drugs that we proposed in our previous work in a severity stage-specific manner using an in vitro BACE1 assay and the effect of a top-ranked drug from the list of our previous work, tetrabenazine (TBZ), in the 5XFAD as an AD mouse model. From our in vitro screening, we detected 2 compounds (clomiphene citrate and Pik-90) that showed statistically significant inhibition against the activity of the BACE1 enzyme. The administration of TBZ at the selected dose and therapeutic regimen in 5XFAD in male and female mice showed no significant effect in behavioral tests using the Y-maze and the ELISA immunoassay of Aß40. To our knowledge, this is the first time the drug tetrabenazine has been tested in the 5XFAD mouse model of AD in a sex-stratified manner. Our results highlight 2 drugs (clomiphene citrate and Pik-90) from our previous computational work for further investigation.

Network-based stage-specific drug repurposing for Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common type of dementia. With no disease-curing drugs available and an ever-growing AD-related healthcare burden, novel approaches for identifying therapies are needed. In this work, we propose stage-specific candidate repurposed drugs against AD by using a novel network-based method for drug repurposing against different stages of AD severity. For each AD stage, this approach a) ranks the candidate repurposed drugs based on a novel network-based score emerging from the weighted sum of connections in a network resembling the structural similarity with failed, approved or currently ongoing drugs b) re-ranks the candidate drugs based on functional, structural and a priori information according to a recently developed method by our group and c) checks and re-ranks for permeability through the Blood Brain Barrier (BBB). Overall, we propose for further experimental validation 10 candidate repurposed drugs for each AD stage comprising a set of 26 elite candidate repurposed drugs due to overlaps between the three AD stages. We applied our methodology in a retrospective way on the known clinical trial drugs till 2016 and we show that we were able to highly rank a drug that did enter clinical trials in the following year. We expect that our proposed network-based drug-repurposing methodology will serve as a paradigm for application for ranking candidate repurposed drugs in other brain diseases beyond AD.

Laser-Induced Morphological and Structural Changes of Cesium Lead Bromide Nanocrystals.

Metal halide perovskite nanocrystals, an emerging class of materials for advanced photonic and optoelectronic applications, are mainly fabricated with colloidal chemistry routes. On the quest for new properties according to application needs, new perovskite systems of various morphologies and levels of doping and alloying have been developed, often also involving post-synthesis reactions. Recently, laser irradiation in liquids has been utilized as a fast method to synthesize or transform materials and interesting laser-induced transformations on nanocrystals were induced. These studies in general have been limited to small nanocrystals (~15 nm). In the case of halide perovskites, fragmentation or anion exchange have been observed in such laser-based processes, but no crystal structure transformations were actually observed or deliberately studied. Nanocrystals are more sensitive to light exposure compared to the corresponding bulk crystals. Additional factors, such as size, morphology, the presence of impurities, and others, can intricately affect the photon absorption and heat dissipation in nanocrystal suspensions during laser irradiation. All these factors can play an important role in the final morphologies and in the time required for these transformations to unfold. In the present work, we have employed a 513 nm femtosecond (fs) laser to induce different transformations in large nanocrystals, in which two phases coexist in the same particle (Cs4PbBr6/CsPbBr3 nanohexagons of ~100 nm), dispersed in dichlorobenzene. These transformations include: (i) the exfoliation of the primary nanohexagons and partial anion exchange; (ii) fragmentation in smaller nanocubes and partial anion exchange; (iii) side-by-side-oriented attachment, fusion, and formation of nanoplatelets and complete anion exchange; (iv) side-by-side attachment, fusion, and formation of nanosheets. Partial or complete Br-Cl anion exchange in the above transformations was triggered by the partial degradation of dichlorobenzene. In addition to the detailed analysis of the various nanocrystal morphologies observed in the various transformations, the structure-photoluminescence relationships for the different samples were analyzed and discussed.

Short Pulse Laser Synthesis of Transition-Metal Dichalcogenide Nanostructures under Ambient Conditions.

The study of inorganic nanometer-scale materials with hollow closed-cage structures, such as inorganic fullerene-like (IF) nanostructures and inorganic nanotubes (INTs), is a rapidly growing field. Numerous kinds of IF nanostructures and INTs were synthesized for a variety of applications, particularly for lubrication, functional coatings, and reinforcement of polymer matrices. To date, such nanostructures have been synthesized mostly by heating a transition metal or oxide thereof in the presence of precursor gases, which are however toxic and hazardous. In this context, one frontier of research in this field is the development of new avenues for the green synthesis of IF structures and INTs, directly from the bulk of layered compounds. In the present work, we demonstrate a simple room-temperature and environmentally friendly approach for the synthesis of IF nanostructures and INTs via ultrashort-pulse laser ablation of a mixture of transition-metal dichalcogenides in bulk form mixed with Pb/PbO, in ambient air. The method can be considered as a synergy of photothermally and photochemically induced chemical transformations. The ultrafast-laser-induced excitation of the material, complemented with the formation of extended hot annealing regions in the presence of the metal catalyst, facilitates the formation of different nanostructures. Being fast, easy, and material-independent, our method offers new opportunities for the synthesis of IF nanostructures and INTs from different bulk metal chalcogenide compounds. On the basis of the capabilities of laser technology as well, this method could advantageously be further developed into a versatile tool for the simultaneous growth and patterning of such nanostructures in preselected positions for a variety of applications.

Solution processed reduced graphene oxide electrodes for organic photovoltaics.

Since the isolation of free standing graphene in 2004, graphene research has experienced a phenomenal growth. Due to its exceptional electronic, optical and mechanical properties, graphene is believed to be the next wonder material for optoelectronics. The enhanced electrical conductivity, combined with its high transparency in the visible and near-infrared regions of the spectrum, enabled graphene to be an ideal low cost indium-tin oxide (ITO) substitute. Solution-processed reduced graphene oxide combines the unique optoelectrical properties of graphene with large area deposition and flexible substrates rendering it compatible with roll-to-roll manufacturing methods. This paper provides an overview of recent research progress in the application and consequent physical-chemical properties of solution-processed reduced graphene oxide-based films as transparent conductive electrodes (TCEs) in organic photovoltaic (OPV) cells. Reduced graphene oxide (rGO) can be effectively utilized as the TCE in flexible OPVs, where the brittle and expensive ITO is incompatible. The prospects and future research trends in graphene-based TCEs are also discussed.

Improving the efficiency of organic photovoltaics by tuning the work function of graphene oxide hole transporting layers.

A facile, fast, non-destructive and roll-to-roll compatible photochemical method for simultaneous partial reduction and doping of graphene oxide (GO) films through ultraviolet laser irradiation in the presence of a Cl2 precursor gas is demonstrated. The photochemical chlorinated GO-Cl films were fully characterized by XPS and Raman measurements, in which grafting of chloride to the edges and the basal plane of GO was confirmed. By tuning the laser exposure time, it is possible to control the doping and reduction levels and therefore to tailor the work function (WF) of the GO-Cl layers from 4.9 eV to a maximum value of 5.23 eV. These WF values match with the HOMO level of most polymer donors employed in OPV devices. Furthermore, high efficiency poly(2,7-carbazole) derivative (PCDTBT):fullerene derivative (PC71BM) based OPVs with GO-Cl as the hole transporting layer (HTL) were demonstrated with a power conversion efficiency (PCE) of 6.56% which is 17.35% and 19.48% higher than that of the pristine GO and PEDOT:PSS based OPV devices, respectively. The performance enhancement was attributed to more efficient hole transportation due to the energy level matching between the GO-Cl and the polymer donor.