Millions of naïve T cells with different TCRs may interact with a peptide-MHC ligand, but very few will activate. Remarkably, this fine control is orchestrated using a limited set of intracellular machinery. It remains unclear whether changes in stimulation strength alter the programme of signalling events leading to T cell activation. Using mass cytometry to simultaneously measure multiple signalling pathways during activation of murine CD8+ T cells, we found a programme of distal signalling events that is shared, regardless of the strength of TCR stimulation. Moreover, the relationship between transcription of early response genes Nr4a1 and Irf8 and activation of the ribosomal protein S6 is also conserved across stimuli. Instead, we found that stimulation strength dictates the rate with which cells initiate signalling through this network. These data suggest that TCR-induced signalling results in a coordinated activation program, modulated in rate but not organization by stimulation strength.
Raw mass cytometry data can be found on the Flow Repository, accession numbers FR-FCM-Z2CX and FR-FCM-Z2CP.Full results of mass cytometry analyses are included as Supplementary File 5.Source data for summary plots of flow cytometry-measured signaling markers in T cells stimulated with peptide-loaded BMDCs (Figure 7a) are included as Figure 7 - Source Data File 1.Analysis code is available athttps://github.com/MarioniLab/SignallingMassCytoStimStrength
- Gillian M Griffiths
- Claire Y Ma
- John C Marioni
- Arianne C Richard
- Claire Y Ma
- John C Marioni
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: Animal experimentation: Experiments were carried out under Project Licence PPL 70/8590. This research has been regulated under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB).
- Michael L Dustin, University of Oxford, United Kingdom
© 2020, Ma et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, i.e., possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer cell lines or characterize intrinsic drug resistance without a priori knowledge of its cause. We hypothesized that cell morphology could provide an unbiased readout of drug resistance. To test this hypothesis, we used HCT116 cells, a mismatch repair-deficient cancer cell line, to isolate clones that were resistant or sensitive to bortezomib, a well-characterized proteasome inhibitor and anticancer drug to which many cancer cells possess intrinsic resistance. We then expanded these clones and measured high-dimensional single-cell morphology profiles using Cell Painting, a high-content microscopy assay. Our imaging- and computation-based profiling pipeline identified morphological features that differed between resistant and sensitive cells. We used these features to generate a morphological signature of bortezomib resistance. We then employed this morphological signature to analyze a set of HCT116 clones (five resistant and five sensitive) that had not been included in the signature training dataset, and correctly predicted sensitivity to bortezomib in seven cases, in the absence of drug treatment. This signature predicted bortezomib resistance better than resistance to other drugs targeting the ubiquitin-proteasome system. Our results establish a proof-of-concept framework for the unbiased analysis of drug resistance using high-content microscopy of cancer cells, in the absence of drug treatment.
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen’s probability of triggering a response, and on the other hand the T-cell receptor’s ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.