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Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single‐cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide‐seq. Collide‐seq revealed the lack of a common mechanism through which fibroblast‐specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide‐seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms. Synopsis: Collide‐seq, a single‐cell protocol for analysing how different reprogramming transcription factors achieve conversion and how cells resolve cell identity conflicts, reveals several fundamental principles of fate conversion: The ability to erase starter cell identity is a common feature of most reprogramming factors, though their mechanisms for fate erasure differ.Reprogramming factor levels and target gene expression show a binary relationship, i.e., a functional threshold for the induction and repression of target genes.Cell identity conflicts are resolved through either factor dominance or generation of a novel transcriptomic status (a collision state) resulting in clear phenotypic consequences. [ABSTRACT FROM AUTHOR]

Single‐cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in‐depth characterization of individual cells by mass spectrometry (MS)‐based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow‐rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10‐fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS‐isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single‐cell proteomes to transcriptome data revealed a stable‐core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra‐high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease. Synopsis: A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable. A highly optimized data independent acquisition powered single‐cell proteomics workflow including sub‐µl sample preparation, very low flow chromatography and trapped ion mobility mass spectrometry (diaPASEF) is presented.Single‐cell proteome analysis is performed by injecting cells one‐by‐one across the cell cycle into the LC‐MS and correctly identifies cell states.Single‐cell proteome information is highly complementary to single‐cell transcriptome information.At the single‐cell level the proteome is quantitatively and qualitatively stable, while the transcriptome is stochastic. [ABSTRACT FROM AUTHOR]

It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis: TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. [ABSTRACT FROM AUTHOR]

This study investigates the tribological behavior of a new class of silicon-disilicide ( Si-( Cr, V) Si2) in situ composites which display eutectic microstructures comprising a Si matrix and reinforcing ( Cr, V) Si2 disilicide phase. Dry, room temperature ball-on-disk tests carried out at various loads ( W = 1-6 N) showed that the specific wear rates of the Si-( Cr, V) Si2 composites ( ka ≈10−5 mm3/Nm) were about an order of magnitude lower than that of unalloyed Si ( ka ≈10−4 mm3/Nm) in the wear regime dominated by lateral cracking. Microscopic analysis of the wear tracks, as well as observations from indentation experiments, revealed that the improved wear resistance of the Si-( Cr, V) Si2 composites at higher loads was due to the activation of crack deflection and bridging toughening mechanisms during the wear process, which results in a rise in the apparent composite fracture toughness with increasing crack size (i.e., a rising R-curve behavior). Analysis of the wear test data in the context of a lateral fracture wear model demonstrates that the enhanced short-crack response of the Si-( Cr, V) Si2 composites during sliding wear can indeed be explained by the incorporation of a sharply rising R-curve relation for the composite fracture toughness. [ABSTRACT FROM AUTHOR]