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Leukocyte dynamics after intracerebral hemorrhage in a living patient reveal rapid adaptations to tissue milieu
Brittany A. Goods, … , Lauren H. Sansing, the ICHseq Investigators
Brittany A. Goods, … , Lauren H. Sansing, the ICHseq Investigators
Published March 22, 2021
Citation Information: JCI Insight. 2021;6(6):e145857. https://doi.org/10.1172/jci.insight.145857.
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Research Article Immunology Neuroscience

Leukocyte dynamics after intracerebral hemorrhage in a living patient reveal rapid adaptations to tissue milieu

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Abstract

Intracerebral hemorrhage (ICH) is a devastating form of stroke with a high mortality rate and few treatment options. Discovery of therapeutic interventions has been slow given the challenges associated with studying acute injury in the human brain. Inflammation induced by exposure of brain tissue to blood appears to be a major part of brain tissue injury. Here, we longitudinally profiled blood and cerebral hematoma effluent from a patient enrolled in the Minimally Invasive Surgery with Thrombolysis in Intracerebral Hemorrhage Evacuation trial, offering a rare window into the local and systemic immune responses to acute brain injury. Using single-cell RNA-Seq (scRNA-Seq), this is the first report to our knowledge that characterized the local cellular response during ICH in the brain of a living patient at single-cell resolution. Our analysis revealed shifts in the activation states of myeloid and T cells in the brain over time, suggesting that leukocyte responses are dynamically reshaped by the hematoma microenvironment. Interestingly, the patient had an asymptomatic rebleed that our transcriptional data indicated occurred prior to detection by CT scan. This case highlights the rapid immune dynamics in the brain after ICH and suggests that sensitive methods such as scRNA-Seq would enable greater understanding of complex intracerebral events.

Authors

Brittany A. Goods, Michael H. Askenase, Erica Markarian, Hannah E. Beatty, Riley S. Drake, Ira Fleming, Jonathan H. DeLong, Naomi H. Philip, Charles C. Matouk, Issam A. Awad, Mario Zuccarello, Daniel F. Hanley, J. Christopher Love, Alex K. Shalek, Lauren H. Sansing, the ICHseq Investigators

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Figure 2

Shifts in prevalence and phenotypes of myeloid cells in hematoma effluent and blood over time.

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Shifts in prevalence and phenotypes of myeloid cells in hematoma effluen...
(A) t-SNE plot of reclustered monocytes, macrophages, and DCs from Figure 1 (n = 8292 cells). The reclustered t-SNE plot is colored by hematoma or blood (left), time after onset (middle), or subcluster identity. (B) Stacked frequency plot of new clusters by hours after onset in hematoma and blood. (C) Top 10 significantly enriched IPA pathways for selected clusters. Myeloid subclusters 4, 2, 5 emerge sequentially in hematoma; myeloid subclusters 3, 1, and 10 emerge sequentially in blood; and subcluster 0 predominates in patient follow-up at 2.5 years and control blood. Remaining clusters are presented in Supplemental Figure 8. Pathways with 3 or more molecules in the query gene list are indicated by an asterisk. RA, rheumatoid arthritis; PRRs, pattern recognition receptors. (D) Violin plot of module scores for an inflammatory monocyte gene signature for each subcluster. All gene modules scored are presented in Supplemental Figure 9. Each subcluster is annotated as predominantly blood (red +) or hematoma (gray +) in origin below the plot. Annotated adjusted P values were calculated using Wilcoxon’s rank-sum test with Benjamini-Hochberg P value correction, and only select comparisons are annotated on the plot (**Padj < 0.001). Full pairwise results for each cluster are shown in Supplemental Table 7.

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