Comparative single-cell analysis of biopsies clarifies pathogenic mechanisms in Klinefelter syndrome.

Klinefelter syndrome (KS), also known as 47, XXY, is characterized by a distinct set of physiological abnormalities, commonly including infertility. The molecular basis for Klinefelter-related infertility is still unclear, largely because of the cellular complexity of the testis and the intricate endocrine and paracrine signaling that regulates spermatogenesis. Here, we demonstrate an analysis framework for dissecting human testis pathology that uses comparative analysis of single-cell RNA-sequencing data from the biopsies of 12 human donors. By comparing donors from a range of ages and forms of infertility, we generate gene expression signatures that characterize normal testicular function and distinguish clinically distinct forms of male infertility. Unexpectedly, we identified a subpopulation of Sertoli cells within multiple individuals with KS that lack transcription from the XIST locus, and the consequence of this is increased X-linked gene expression compared to all other KS cell populations. By systematic assessment of known cell signaling pathways, we identify 72 pathways potentially active in testis, dozens of which appear upregulated in KS. Altogether our data support a model of pathogenic changes in interstitial cells cascading from loss of X inactivation in pubertal Sertoli cells and nominate dosage-sensitive factors secreted by Sertoli cells that may contribute to the process. Our findings demonstrate the value of comparative patient analysis in mapping genetic mechanisms of disease and identify an epigenetic phenomenon in KS Sertoli cells that may prove important for understanding causes of infertility and sex chromosome evolution.

Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. RESULTS: We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP.