CD4 and CD8 mark helper and cytotoxic T cell lineages, respectively, and serve as coreceptors for MHC-restricted TCR recognition. How coreceptor expression is matched with TCR specificity is central to understanding CD4/CD8 lineage choice, but visualising coreceptor gene activity in individual selection intermediates has been technically challenging. It therefore remains unclear whether the sequence of coreceptor gene expression in selection intermediates follows a stereotypic pattern, or is responsive to signaling. Here we use single cell RNA sequencing (scRNA-seq) to classify mouse thymocyte selection intermediates by coreceptor gene expression. In the unperturbed thymus, Cd4+Cd8a- selection intermediates appear before Cd4-Cd8a+ selection intermediates, but the timing of these subsets is flexible according to the strength of TCR signals. Our data show that selection intermediates discriminate MHC class prior to the loss of coreceptor expression and suggest a model where signal strength informs the timing of coreceptor gene activity and ultimately CD4/CD8 lineage choice.
Clinical case reports are the `eyewitness reports’ of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally a case report has a single main finding that represents the reason for writing up the report in the first place. In the present study, we present the results of manual annotation carried out by two individuals on 500 randomly sampled case reports. This corpus contains main finding sentences extracted from title, abstract and full-text of the same article that can be regarded as semantically related and are often paraphrases. The final reconciled corpus of 416 articles comprises an open resource for further study. This is the first step in establishing text mining models and tools that can identify main finding sentences in an automated fashion, and in measuring quantitatively how similar any two main findings are. We envision that case reports in PubMed may be automatically indexed by main finding, so that users can carry out information queries for specific main findings (rather than general topics)—and given one case report, a user can retrieve those having the most similar main findings. The metric of main finding similarity may also potentially be relevant to the modeling of paraphrasing, summarization and entailment within the biomedical literature.
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