Adaptive cellular immunity requires accurate self-vs. nonself-discrimination to protect against infections and tumorous transformations while at the same time excluding autoimmunity. This vital capability is programmed in the thymus through selection of αβT-cell receptors (αβTCRs) recognizing peptides bound to MHC molecules (pMHC). Here, we show that the pre-TCR (preTCR), a pTα-β heterodimer appearing before αβTCR expression, directs a previously unappreciated initial phase of repertoire selection. Contrasting with the ligandindependent model of preTCR function, we reveal through NMR and bioforce-probe analyses that the β-subunit binds pMHC using Vβ complementarity-determining regions as well as an exposed hydrophobic Vβ patch characteristic of the preTCR. Force-regulated single bonds akin to those of αβTCRs but with more promiscuous ligand specificity trigger calcium flux. Thus, thymic development involves sequential β-and then, αβ-repertoire tuning, whereby preTCR interactions with self pMHC modulate early thymocyte expansion, with implications for β-selection, immunodominant peptide recognition, and germ line-encoded MHC interaction.pre-T-cell receptor | NMR spectroscopy | biomembrane force probe | thymic development | repertoire selection

A new automated microfluidic platform can deliver a wide range of mechanical stimuli for functional neural imaging in C. elegans.

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.

Health and longevity in all organisms are strongly influenced by the environment. To fully understand how environmental factors interact with genetic and stochastic factors to modulate the aging process, it is crucial to precisely control environmental conditions for long-term studies. In the commonly used model organism Caenorhabditis elegans, existing assays for healthspan and lifespan have inherent limitations, making it difficult to perform large-scale longitudinal aging studies under precise environmental control. To address these constraints, we developed the Health and Lifespan Testing Hub (HeALTH), an automated, microfluidic-based system for robust longitudinal behavioral monitoring. Our system provides long-term (i.e. entire lifespan) spatiotemporal environmental control. We demonstrate healthspan and lifespan studies under a variety of genetic and environmental perturbations while observing how individuality plays a role in the aging process. This system is generalizable beyond aging research, particularly for short- or long-term behavioral assays, and could be adapted for other model systems.