Additional file 1: of SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes

Contains supplementary figures and tables. (PDF 817 kb

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database - Fig 1

<p>(A) Number of tools in the scRNA-tools database over time. Since the scRNA-seq tools database was started in September 2016 more than 160 new tools have been released. (B) Publication status of tools in the scRNA-tools database. Over half of the tools in the full database have at least one published peer-revirew paper while another third are described in preprints. (C) When stratified by the date tools were added to the database we see that the majority of tools added before October 2016 are published, while around half of newer tools are available only as preprints. Newer tools are also more likely to be unpublished in any form. (D) The majority of tools are available using either the R or Python programming languages. (E) Most tools are released under a standard open-source software license, with variants of the GNU Public License (GPL) being the most common. However licenses could not be found for a large proportion of tools. Up-to-date versions of these plots (with the exception of C) are available on the analysis page of the scRNA-tools website (<a href="https://www.scrna-tools.org/analysis" target="_blank">https://www.scrna-tools.org/analysis</a>).</p

Phases of a typical unsupervised scRNA-seq analysis process.

<p>In Phase 1 (data acquisition) raw sequencing reads are converted into a gene by cell expression matrix. For many protocols this requires the alignment of genes to a reference genome and the assignment and de-duplication of Unique Molecular Identifiers (UMIs). The data is then cleaned (Phase 2) to remove low-quality cells and uninformative genes, resulting in a high-quality dataset for further analysis. The data can also be normalised and missing values imputed during this phase. Phase 3 assigns cells, either in a discrete manner to known (classification) or unknown (clustering) groups or to a position on a continuous trajectory. Interesting genes (eg. differentially expressed, markers, specific patterns of expression) are then identified to explain these groups or trajectories (Phase 4).</p

Additional file 1: Figures S1–S17 of Splatter: simulation of single-cell RNA sequencing data

Diagrams of other simulation models, Splatter comparison output for all datasets, example non-linear gene, dispersion estimate correction, mean-zeros fit, benchmarking, and processing times (PDF 17991 kb

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database - Fig 3

<p>(A) Categories of tools in the scRNA-tools database. Each tool can be assigned to multiple categories based on the tasks it can complete. Categories associated with multiple analysis phases (visualisation, dimensionality reduction) are among the most common, as are categories associated with the cell assignment phase (ordering, clustering). (B) Changes in analysis categories over time, comparing tools added before and after October 2016. There have been significant increases in the percentage of tools associated with visualisation, dimensionality reduction, gene networks and simulation. Categories including expression patterns, ordering and interactivity have seen relative decreases. (C) Changes in the percentage of tools associated with analysis phases over time. The percentage of tools involved in the data acquisition and data cleaning phases have increased, as have tools designed for alternative analysis tasks. The gene identification phase has seen a relative decrease in the number of tools. (D) The number of categories associated with each tools in the scRNA-tools database. The majority of tools perform few tasks. (E) Most tools that complete many tasks are relatively recent.</p

Additional file 2: of Splatter: simulation of single-cell RNA sequencing data

Table of the median absolute deviations used to produce Fig. 4 in CSV format. (CSV 37 kb

Additional file 3: of Splatter: simulation of single-cell RNA sequencing data

Session information. Details of the R environment and packages used for analysis. (PDF 118 kb

Descriptions of categories for tools in the scRNA-tools database.

<p>Descriptions of categories for tools in the scRNA-tools database.</p

MOESM1 of Limb patterning genes and heterochronic development of the emu wing bud

Additional file 1. Cell counting using ImageJ (Fiji). Stage 21 emu forelimb bud is shown for example

data_sheet_1_Cord Blood CD8+ T Cells Have a Natural Propensity to Express IL-4 in a Fatty Acid Metabolism and Caspase Activation-Dependent Manner.csv

<p>How T cells differentiate in the neonate may critically determine the ability of the infant to cope with infections, respond to vaccines and avert allergies. Previously, we found that naïve cord blood CD4⁺ T cells differentiated toward an IL-4-expressing phenotype when activated in the presence of TGF-β and monocyte-derived inflammatory cytokines, the latter are more highly secreted by infants who developed food allergy. Here, we show that in the absence of IL-2 or IL-12, naïve cord blood CD8⁺ T cells have a natural propensity to differentiate into IL-4-producing non-classic T_C2 cells when they are activated alone, or in the presence of TGF-β and/or inflammatory cytokines. Mechanistically, non-classic T_C2 development is associated with decreased expression of IL-2 receptor alpha (CD25) and glycolysis, and increased fatty acid metabolism and caspase-dependent cell death. Consequently, the short chain fatty acid, sodium propionate (NaPo), enhanced IL-4 expression, but exogenous IL-2 or pan-caspase inhibition prevented IL-4 expression. In children with endoscopically and histologically confirmed non-inflammatory bowel disease and non-infectious pediatric idiopathic colitis, the presence of TGF-β, NaPo, and IL-1β or TNF-α promoted T_C2 differentiation in vitro. In vivo, colonic mucosa of children with colitis had significantly increased expression of IL-4 in CD8⁺ T cells compared with controls. In addition, activated caspase-3 and IL-4 were co-expressed in CD8⁺ T cells in the colonic mucosa of children with colitis. Thus, in the context of colonic inflammation and limited IL-2 signaling, CD8⁺ T cells differentiate into non-classic T_C2 that may contribute to the pathology of inflammatory/allergic diseases in children.</p