Figure 1 Experimental workflow and predictive modelling

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CsEN components reveal precisely timed cellular programs that characterize the dynamic changes of the peripheral immune system over the course of pregnancy.

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Figure 1 Experimental workflow and predictive modelling Figure 1 Experimental workflow and predictive modelling. (A) Twenty-four patients with acute ischaemic stroke ... Figure 1 Experimental workflow and predictive modelling. (A) Twenty-four patients with acute ischaemic stroke participated in the study. A whole blood sample was obtained at up to nine time points (1, 2, 3, 5, 7, 14, 30, 90, and 365 days) following stroke onset. Samples were processed and analysed using mass cytometry (CyTOF). (B) Representation of blood collection from each of the 24 enrolled subjects. Blue dots indicate days on which blood was obtained for each subject. (C) The mass cytometry data formed a correlation network that visually segregated into 18 communities containing inter-correlated immune features that changed together during the stroke recovery process. Clusters were annotated based on immune feature attributes (cell subset, signalling property or frequency) that were most commonly represented within each community. pDC = plasmacytoid dendritic cells; ncMCs = CD14<sup>−</sup>CD16<sup>+</sup> non-classical monocytes. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 March 2019, Pages 978–991, https://doi.org/10.1093/brain/awz022 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2 An elastic net analysis identifies three phases characterizing the systemic immune response to acute ... Figure 2 An elastic net analysis identifies three phases characterizing the systemic immune response to acute ischaemic stroke. (A–C) Comparison of the immunological dataset generated at each time point to the 1-year dataset using elastic net analysis provided three cross-validated elastic net models (P < 0.01) that characterized distinct phases of the systemic immune response to stroke. Features selected in the acute (Day 2 versus Day 360, A), intermediate (Day 5 versus Day 365, B), and late phases (Day 90 versus Day 365, C) elastic net models are graphically overlaid on the immune network representing the entire immunological dataset. Red nodes indicate features that are elevated compared to the 1-year time point and blue nodes indicate features that are decreased. Size of node corresponds to the statistical correlation between features and the two time points that make up each model, and intensity of red or blue colour represents the absolute value of elastic net model coefficients at that node. EN = elastic net. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 March 2019, Pages 978–991, https://doi.org/10.1093/brain/awz022 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3 Time-projected elastic net model values reveals the relative magnitude of the acute, intermediate and late ... Figure 3 Time-projected elastic net model values reveals the relative magnitude of the acute, intermediate and late inflammatory phase. (A) Time span covered by each of the three models. (B–D) Immune features selected by the acute, intermediate and late elastic net models were quantified at each time point and a time-projected value was inferred for each elastic net model. Box plots depict range of elastic net model values over time, including maximum, minimum, median, and interquartile range, for the (B) acute phase model, (C) intermediate phase model, and (D) late phase model. Time points used to derive each elastic net models are highlighted in blue. EN = elastic net. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 March 2019, Pages 978–991, https://doi.org/10.1093/brain/awz022 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 4 Elastic net components reveal time-specific alterations in innate and adaptive immune responses after acute ... Figure 4 Elastic net components reveal time-specific alterations in innate and adaptive immune responses after acute ischaemic stroke. The components of each elastic net model were nested in communities of highly correlated features that changed concordantly after stroke. The three most significant elastic net components are shown for (A) the acute phase model, (B) the intermediate phase model, and (C) the late phase model. Diagrams on the left highlight communities containing the most informative elastic net components. Graphs to the right depict the value of elastic net components at each time point (black dots represent individual samples; red lines and grey background represent median and 95% confidence interval, respectively). EN = elastic net; cMCs = classical monocytes; pDCs = plasmacytoid dendritic cells. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 March 2019, Pages 978–991, https://doi.org/10.1093/brain/awz022 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 5 Day 2 immune features comprising the acute phase elastic net model correlate with cognitive trajectories from ... Figure 5 Day 2 immune features comprising the acute phase elastic net model correlate with cognitive trajectories from Days 90 to 365. (A) Trajectory of MoCA scores between Days 90 and 365 after stroke. Cognitive stability or improvement is represented by black lines, while cognitive decline is indicated by red lines. (B) Stroke volume, measured in log(cm<sup>3</sup>), did not correlate with changes in MoCA scores (ΔMoCA). (C) Elastic net model values of the acute phase model (Fig. 2A) negatively correlate with ΔMoCA between Days 90 and 365 (Spearman’s r = −0.692, Bonferroni-corrected P = 0.039). (D) Acute phase elastic net values correlated with stroke volume (Spearman’s r = 0.569, Bonferroni-corrected P = 0.018). EN = elastic net. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 March 2019, Pages 978–991, https://doi.org/10.1093/brain/awz022 The content of this slide may be subject to copyright: please see the slide notes for details.