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Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p = 6.15 × 10 -76 , r = 0.24; cohen's D = 2.6). Building on this, we developed an augmented annotation algorithm (single cell Annotation via Literature Encoding, or scALE) that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 133 clusters from nine datasets of human breast, colon, heart, joint, ovary, prostate, skin, and small intestine tissues. With the optimized settings, the true cellular identity matched the top prediction in 59% of tested clusters and was present among the top five predictions for 91% of clusters. scALE slightly outperformed an existing method for reference data driven automated cluster annotation, and we demonstrate that integration of scALE can meaningfully improve the annotations derived from such methods. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of well-studied cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature-derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

In our efforts toward producing environmentally responsible but highly stable bioelectrodes with high electroactivities, we report here a simple, inexpensive, autoclavable high sensitivity biosensor based on enzyme-polymer nanogels. Met-hemoglobin (Hb) is stabilized by wrapping it in high molecular weight poly(acrylic acid) (PAA, M(W) 450k), and the resulting nanogels abbreviated as Hb-PAA-450k, withstood exposure to high temperatures for extended periods under steam sterilization conditions (122 °C, 10 min, 17-20 psi) without loss of Hb structure or its peroxidase-like activities. The bioelectrodes prepared by coating Hb-PAA-450k nanogels on glassy carbon showed well-defined quasi-reversible redox peaks at -0.279 and -0.334 V in cyclic voltammetry (CV) and retained >95% electroactivity after storing for 14 days at room temperature. Similarly, the bioelectrode showed ~90% retention in electrochemical properties after autoclaving under steam sterilization conditions. The ultra stable bioelectrode was used to detect hydrogen peroxide and demonstrated an excellent detection limit of 0.5 μM, the best among the Hb-based electrochemical biosensors. This is the first electrochemical demonstration of steam-sterilizable, storable, modular bioelectrode that undergoes reversible-thermal denaturation and retains electroactivity for protein based electrochemical applications.