Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning.

Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.

Pepstatin-Based Probes for Photoaffinity Labeling of Aspartic Proteases and Application to Target Identification.

Aspartic proteases are a small class of proteases implicated in a wide variety of human diseases. Covalent chemical probes for photoaffinity labeling (PAL) of these proteases are underdeveloped. We here report a full on-resin synthesis of clickable PAL probes based on the natural product inhibitor pepstatin incorporating a minimal diazirine reactive group. The position of this group in the inhibitor determines the labeling efficiency. The most effective probes sensitively detect cathepsin D, a biomarker for breast cancer, in cell lysates. Moreover, through chemical proteomics experiments and deep learning algorithms, we identified sequestosome-1, an important player in autophagy, as a direct interaction partner and substrate of cathepsin D.