Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

In molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables

Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots

Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using methods like PCA, t-SNE, or UMAP. Given a cluster of cells, one frequently searches for the genes highly expressed specifically in that cluster. At this point, visualization is usually replaced by studying a list of differentially expressed genes. Association Plots are derived from correspondence analysis and constitute a planar visualization of the features which characterize a given cluster of observations. We have adapted Association Plots to address the challenge of visualizing cluster-specific genes in large single-cell data sets. Our method is made available as a free R package called APL. We demonstrate the application of APL and Association Plots to single-cell RNA-seq data on two example data sets. First, we present how to delineate novel marker genes using Association Plots with the example of Peripheral Blood Mononuclear Cell data. Second, we show how to apply Association Plots for annotating cell clusters to known cell types using Association Plots and a predefined list of marker genes. To do this we will use data from the human cell atlas of fetal gene expression. Results from Association Plots will also be compared to methods for deriving differentially expressed genes, and we will show the integration of APL with Gene Ontology Enrichment

Enhanced cortical neural stem cell identity through short SMAD and WNT inhibition in human cerebral organoids facilitates emergence of outer radial glial cells

Cerebral organoids exhibit broad regional heterogeneity accompanied by limited cortical cellular diversity despite the tremendous upsurge in derivation methods, suggesting inadequate patterning of early neural stem cells (NSCs). Here we show that a short and early Dual SMAD and WNT inhibition course is necessary and sufficient to establish robust and lasting cortical organoid NSC identity, efficiently suppressing non-cortical NSC fates, while other widely used methods are inconsistent in their cortical NSC-specification capacity. Accordingly, this method selectively enriches for outer radial glia NSCs, which cyto-architecturally demarcate well-defined outer sub-ventricular-like regions propagating from superiorly radially organized, apical cortical rosette NSCs. Finally, this method culminates in the emergence of molecularly distinct deep and upper cortical layer neurons, and reliably uncovers cortex-specific microcephaly defects. Thus, a short SMAD and WNT inhibition is critical for establishing a rich cortical cell repertoire that enables mirroring of fundamental molecular and cyto-architectural features of cortical development and meaningful disease modelling

Anisotropic expansion of hepatocyte lumina enforced by apical bulkheads

Lumen morphogenesis results from the interplay between molecular pathways and mechanical forces. In several organs, epithelial cells share their apical surfaces to form a tubular lumen. In the liver, however, hepatocytes share the apical surface only between adjacent cells and form narrow lumina that grow anisotropically, generating a 3D network of bile canaliculi (BC). Here, by studying lumenogenesis in differentiating mouse hepatoblasts in vitro, we discovered that adjacent hepatocytes assemble a pattern of specific extensions of the apical membrane traversing the lumen and ensuring its anisotropic expansion. These previously unrecognized structures form a pattern, reminiscent of the bulkheads of boats, also present in the developing and adult liver. Silencing of Rab35 resulted in loss of apical bulkheads and lumen anisotropy, leading to cyst formation. Strikingly, we could reengineer hepatocyte polarity in embryonic liver tissue, converting BC into epithelial tubes. Our results suggest that apical bulkheads are cell-intrinsic anisotropic mechanical elements that determine the elongation of BC during liver tissue morphogenesis

Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions

Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hif1a) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes