Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for ...optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits.
Here, we compare gene expression and cellular composition of single-cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample, we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37 °C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an underrepresentation of T, B, and NK lymphocytes in the single-nucleus libraries.
Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single-cell experiments to make an informed choice.
Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key ...transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.
High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy ...development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, "Random Forest" estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
MicroRNAs (miRNAs) are short single stranded noncoding RNAs that suppress gene expression through either translational repression or degradation of target mRNAs. The annealing between messenger RNAs ...and 5' seed region of miRNAs is believed to be essential for the specific suppression of target gene expression. One miRNA can have several hundred different targets in a cell. Rapidly accumulating evidence suggests that many miRNAs are involved in cell cycle regulation and consequentially play critical roles in carcinogenesis.
Introduction of synthetic miR-107 or miR-185 suppressed growth of the human non-small cell lung cancer cell lines. Flow cytometry analysis revealed these miRNAs induce a G1 cell cycle arrest in H1299 cells and the suppression of cell cycle progression is stronger than that by Let-7 miRNA. By the gene expression analyses with oligonucleotide microarrays, we find hundreds of genes are affected by transfection of these miRNAs. Using miRNA-target prediction analyses and the array data, we listed up a set of likely targets of miR-107 and miR-185 for G1 cell cycle arrest and validate a subset of them using real-time RT-PCR and immunoblotting for CDK6.
We identified new cell cycle regulating miRNAs, miR-107 and miR-185, localized in frequently altered chromosomal regions in human lung cancers. Especially for miR-107, a large number of down-regulated genes are annotated with the gene ontology term 'cell cycle'. Our results suggest that these miRNAs may contribute to regulate cell cycle in human malignant tumors.
Abstract
Motivation
Single-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale ...transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process.
Results
We introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision.
Availability and implementation
scMatch (Python code) and the FANTOM5 reference dataset are freely available to the research community here https://github.com/forrest-lab/scMatch.
Supplementary information
Supplementary data are available at Bioinformatics online.
Organic light-emitting diodes are a major driving force of the current information display revolution due to their low power consumption and potentially long operational lifetime. Although ...electrophosphorescent organic emitters have significantly lower power consumption than fluorescent emitters, the short lifetime of electrophosphorescent blue devices has prevented their application in displays for more than a decade. Here, we demonstrate a novel blue electrophosphorescent device with a graded dopant concentration profile in a broadened emissive layer, leading to a lower exciton density compared with a conventional device. Thus, triplet-polaron annihilation that leads to long-term luminescent degradation is suppressed, resulting in a more than threefold lifetime improvement. When this strategy is applied to a two-unit stacked device, we demonstrate a lifetime of 616±10 h (time to 80% of the 1,000 cd m(-2) initial luminance) with chromaticity coordinates of 0.15, 0.29, representing a tenfold lifetime improvement over a conventional blue electrophosphorescent device.
The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample ...information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). This resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.
Optical tracking is often combined with conventional flat panel solar cells to maximize electrical power generation over the course of a day. However, conventional trackers are complex and often ...require costly and cumbersome structural components to support system weight. Here we use kirigami (the art of paper cutting) to realize novel solar cells where tracking is integral to the structure at the substrate level. Specifically, an elegant cut pattern is made in thin-film gallium arsenide solar cells, which are then stretched to produce an array of tilted surface elements which can be controlled to within ±1°. We analyze the combined optical and mechanical properties of the tracking system, and demonstrate a mechanically robust system with optical tracking efficiencies matching conventional trackers. This design suggests a pathway towards enabling new applications for solar tracking, as well as inspiring a broader range of optoelectronic and mechanical devices.
Most of the mammalian genome is transcribed. This generates a vast repertoire of transcripts that includes protein-coding messenger RNAs, long non-coding RNAs (lncRNAs) and repetitive sequences, such ...as SINEs (short interspersed nuclear elements). A large percentage of ncRNAs are nuclear-enriched with unknown function. Antisense lncRNAs may form sense-antisense pairs by pairing with a protein-coding gene on the opposite strand to regulate epigenetic silencing, transcription and mRNA stability. Here we identify a nuclear-enriched lncRNA antisense to mouse ubiquitin carboxy-terminal hydrolase L1 (Uchl1), a gene involved in brain function and neurodegenerative diseases. Antisense Uchl1 increases UCHL1 protein synthesis at a post-transcriptional level, hereby identifying a new functional class of lncRNAs. Antisense Uchl1 activity depends on the presence of a 5' overlapping sequence and an embedded inverted SINEB2 element. These features are shared by other natural antisense transcripts and can confer regulatory activity to an artificial antisense to green fluorescent protein. Antisense Uchl1 function is under the control of stress signalling pathways, as mTORC1 inhibition by rapamycin causes an increase in UCHL1 protein that is associated to the shuttling of antisense Uchl1 RNA from the nucleus to the cytoplasm. Antisense Uchl1 RNA is then required for the association of the overlapping sense protein-coding mRNA to active polysomes for translation. These data reveal another layer of gene expression control at the post-transcriptional level.
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