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alona: a web server for single-cell RNA-seq analysis.
SUMMARY: Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes. AVAILABILITY AND IMPLEMENTATION: The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development.
Single-cell RNA-seq is a powerful technique. Nevertheless, there are important limitations, including the technical challenges of breaking down an organ or tissue into a single-cell suspension. Invariably, this has required enzymatic incubation at 37°C, which can be expected to result in artifactual changes in gene expression patterns. Here, we describe a dissociation method that uses a protease with high activity in the cold, purified from a psychrophilic microorganism. The entire procedure is carried out at 6°C or colder, at which temperature mammalian transcriptional machinery is largely inactive, thereby effectively 'freezing in' the in vivo gene expression patterns. To test this method, we carried out RNA-seq on 20,424 single cells from postnatal day 1 mouse kidneys, comparing the results of the psychrophilic protease method with procedures using 37°C incubation. We show that the cold protease method provides a great reduction in gene expression artifacts. In addition, the results produce a single-cell resolution gene expression atlas of the newborn mouse kidney, an interesting time in development when mature nephrons are present yet nephrogenesis remains extremely active.
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Single-cell transcriptome atlases of soybean root and mature nodule reveal new regulatory programs that control the nodulation process.
The soybean root system is complex. In addition to being composed of various cell types, the soybean root system includes the primary root, the lateral roots, and the nodule, an organ in which mutualistic symbiosis with N-fixing rhizobia occurs. A mature soybean root nodule is characterized by a central infection zone where atmospheric nitrogen is fixed and assimilated by the symbiont, resulting from the close cooperation between the plant cell and the bacteria. To date, the transcriptome of individual cells isolated from developing soybean nodules has been established, but the transcriptomic signatures of cells from the mature soybean nodule have not yet been characterized. Using single-nucleus RNA-seq and Molecular Cartography technologies, we precisely characterized the transcriptomic signature of soybean root and mature nodule cell types and revealed the co-existence of different sub-populations of B. diazoefficiens-infected cells in the mature soybean nodule, including those actively involved in nitrogen fixation and those engaged in senescence. Mining of the single-cell-resolution nodule transcriptome atlas and the associated gene co-expression network confirmed the role of known nodulation-related genes and identified new genes that control the nodulation process. For instance, we functionally characterized the role of GmFWL3, a plasma membrane microdomain-associated protein that controls rhizobial infection. Our study reveals the unique cellular complexity of the mature soybean nodule and helps redefine the concept of cell types when considering the infection zone of the soybean nodule.
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SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.
Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
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Comprehensive Identification and Spatial Mapping of Habenular Neuronal Types Using Single-Cell RNA-Seq.
The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ∼13,000 habenular cells with 4× cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions.
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Atlas of the human intestine.
In this issue of JEM, Wang et al. (https://doi.org/10.1084/jem.20191130), using a single-cell RNA-seq approach, establish an atlas of human colon, rectum, and ileum epithelial cells. Their study reveals that different regions have specialized nutrient absorption preferences, microbe defenses, and endocrine function. They also identify new markers for a variety of cell types.
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[A review on integration methods for single-cell data].
The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
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A single-cell RNA-seq atlas of Schistosoma mansoni identifies a key regulator of blood feeding.
Schistosomiasis is a neglected tropical disease that infects 240 million people. With no vaccines and only one drug available, new therapeutic targets are needed. The causative agents, schistosomes, are intravascular flatworm parasites that feed on blood and lay eggs, resulting in pathology. The function of the parasite's various tissues in successful parasitism are poorly understood, hindering identification of therapeutic targets. Using single-cell RNA sequencing (RNA-seq), we characterize 43,642 cells from the adult schistosome and identify 68 distinct cell populations, including specialized stem cells that maintain the parasite's blood-digesting gut. These stem cells express the gene hnf4, which is required for gut maintenance, blood feeding, and pathology in vivo. Together, these data provide molecular insights into the organ systems of this important pathogen and identify potential therapeutic targets.
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PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.
Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbour (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif outperform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.
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Into the multiverse: advances in single-cell multiomic profiling.
Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.