Heterogeneous, delayed-onset killing by multiple-hitting T cells: Stochastic simulations to assess methods for analysis of imaging data

Richard J Beck, Dario I Bijker and Joost B Beltman

PLOS Computational Biology, 2020, vol. 16, issue 7, 1-25

Abstract: Although quantitative insights into the killing behaviour of Cytotoxic T Lymphocytes (CTLs) are necessary for the rational design of immune-based therapies, CTL killing function remains insufficiently characterised. One established model of CTL killing treats CTL cytotoxicity as a Poisson process, based on the assumption that CTLs serially kill antigen-presenting target cells via delivery of lethal hits, each lethal hit corresponding to a single injection of cytotoxic proteins into the target cell cytoplasm. Contradicting this model, a recent in vitro study of individual CTLs killing targets over a 12-hour period found significantly greater heterogeneity in CTL killing performance than predicted by Poisson-based killing. The observed killing process was dynamic and varied between CTLs, with the best performing CTLs exhibiting a marked increase in killing during the final hours of the experiments, along with a “burst killing” kinetic. Despite a search for potential differences between CTLs, no mechanistic explanation for the heterogeneous killing kinetics was found. Here we have used stochastic simulations to assess whether target cells might require multiple hits from CTLs before undergoing apoptosis, in order to verify whether multiple-hitting could explain the late onset, burst killing dynamics observed in vitro. We found that multiple-hitting from CTLs was entirely consistent with the observed killing kinetics. Moreover, the number of available targets and the spatiotemporal kinetics of CTL:target interactions influenced the realised CTL killing rate. We subsequently used realistic, spatial simulations to assess methods for estimating the hitting rate and the number of hits required for target death, to be applied to microscopy data of individual CTLs killing targets. We found that measuring the cumulative duration of individual contacts that targets have with CTLs would substantially improve accuracy when estimating the killing kinetics of CTLs.Author summary: The immune system plays an important role in controlling infections and tumours. Knowledge about the mechanisms through which the immune system accomplishes this can be exploited to develop immunotherapies. A pivotal mechanism involves killing of target cells by Cytotoxic T Lymphocytes (CTLs), yet limited quantitative knowledge on this process has so far been obtained, especially with respect to the killing capacity of individual CTLs. Recent results suggest that single CTLs exhibit substantial heterogeneity in killing capacity, even amongst clonal CTL populations. Here, we developed stochastic simulations of single CTLs killing small populations of target cells, showing that multiple-hitting can indeed lead to apparently heterogeneous killing kinetics between otherwise identical CTLs. We subsequently generated realistic artificial data using spatial simulations to study how multiple-hit killing parameters could be retrieved from future in vitro or in vivo time-lapse imaging data. Killing parameters were not identifiable when only data on number of killed target cells over time was available. Instead, we show that extraction of killing parameters is substantially improved if the cumulative contact times of CTLs with both killed and surviving target cells are monitored over time, and we offer an approach to fit such data in the future.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007972 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 07972&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007972

DOI: 10.1371/journal.pcbi.1007972

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().