bioRxiv preprint doi: https://doi.org/10.1101/2020.11.10.375675; this version posted November 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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

Brittany A. Goods, PhD1,2*, Michael H. Askenase, PhD3*, Erica Markarian4, Hannah E. Beatty3, Riley Drake1, Ira Fleming1, Jonathan H. DeLong3, Naomi H. Philip5, Charles C. Matouk, MD6, Issam A. Awad, MD7, Mario Zuccarello, MD8, Daniel F. Hanley, MD9, J. Christopher Love, PhD2,4,10,#, Alex K. Shalek, PhD1,2,4,11,12,13,14,#, and Lauren H. Sansing, MD, MS3,5,15,#, on behalf of the ICHseq Investigators

1Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology 2Broad Institute, Harvard University & Massachusetts Institute of Technology 3Department of Neurology, Yale University School of Medicine 4Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology 5Department of Immunobiology, Yale University School of Medicine 6Department of Neurosurgery, Yale University School of Medicine 7Department of Neurosurgery, University of Chicago 8Department of Neurosurgery, University of Cincinnati 9Brain Injury Outcomes Division, Johns Hopkins School of Medicine 10Department of Chemical Engineering, Massachusetts Institute of Technology 11Ragon Institute, Harvard University, Massachusetts Institute of Technology, & Massachusetts General Hospital 12Division of Health Science & Technology, Harvard Medical School 13Program in Computational & Systems Biology, Massachusetts Institute of Technology 14Department of Immunology, Massachusetts General Hospital 15 Human and Translational Immunology Program, Yale School of Medicine *equal #equal

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Corresponding Authors: Lauren Sansing, [email protected], Alex K. Shalek, [email protected], J. Christopher Love, [email protected]

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, particularly over time, 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 Haemorrhage Evacuation (MISTIEIII) trial, offering a rare window into the local and systemic immune responses to acute brain injury. Using single-cell RNA- sequencing, we characterized the local cellular response during ICH in the brain of a living patient at single-cell resolution for the first time. Our analysis revealed rapid 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 re-bleed (second local exposure to blood) that our transcriptional data indicated occurred more than 30 hours prior to detection by CT scan. This case highlights the rapid immune dynamics in the brain after ICH and suggests that sensitive methods like scRNA-seq can inform our understanding of complex intracerebral events.

INTRODUCTION Intracerebral hemorrhage (ICH), brain bleeding often caused by hypertension, has a worldwide incidence of over 3 million cases per year, limited treatment options, and high mortality rate (40-60% worldwide).1,2 To date, surgical approaches to remove the hemorrhage have not improved functional outcomes.3,4 The Phase III clinical trial of Minimally Invasive Surgery with Thrombolysis in Intracerebral Hemorrhage Evacuation (MISTIE III) was designed to test whether minimally invasive catheter evacuation followed by thrombolysis

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would improve functional outcome in patients with ICH. The trial’s approach used image-guided minimally invasive surgery to aspirate the liquid component of the hemorrhage followed by placement of a sutured indwelling catheter to liquify and drain remaining hemorrhage over several days with the aid of recombinant tissue plasminogen activator (tPA). The trial found that surgery was safe and reduced mortality, but did not improve overall functional outcomes 365 days after ICH.5 As part of a trial sub-study (ICHseq), daily samples of both hematoma effluent from the catheter and peripheral blood were collected. These samples provided a unique opportunity to study the immune response to ICH over time in living patients. Here, we describe longitudinal cellular characterization of hematoma effluent and blood from a patient who was enrolled in MISTIE III using single-cell RNA-sequencing (scRNA-seq).6 We define distinct leukocyte phenotypes that may dynamically reflect local inflammatory responses in the brain as well as the emergence of cells resembling peripheral blood leukocytes that we propose identify the onset of repeat tissue exposure to blood (in this case, an asymptomatic re-bleeding event) later detected by CT scan. Additionally, we show that leukocyte transcriptional programs in the patient’s blood return to a baseline comparable to age-matched control blood 2.5 years after ICH. This case report is the first longitudinal single-cell genomic characterization of cellular infiltrate in acute brain injury in a living patient and, importantly, underscores the utility of single-cell analyses for understanding dynamic immune responses in the brain and other tissues.

RESULTS AND DISCUSSION A 74-year old female with hypertension presented to the emergency room with difficulty speaking and right-sided weakness (Supplemental Table 1). Her initial National Institutes of Health Stroke Scale Score (NIHSS) was a 19, and symptoms included aphasia and right-sided hemiplegia, sensory loss, and visual field deficit. A CT scan of the brain showed a large left ICH. Two days after ICH onset, she was enrolled into the MISTIE III trial and randomized to the surgical

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arm. At 51 hours post-hemorrhage, 45 mL of liquid hematoma were aspirated and the drainage catheter was placed. The following day, instillation of tPA (1 mg every 8 hours) was initiated per trial protocol with subsequent effective drainage of the remaining hematoma (Figure 1A). Beginning on day three post- hemorrhage, an increasing volume of drainage was noted from the catheter and tPA was discontinued. The patient consistently improved neurologically. The catheter continued to drain the hematoma for two more days before being removed. The patient was subsequently discharged to an acute rehabilitation facility. Thirty days after ICH onset, she had improved to mild aphasia (difficulty speaking), right visual field deficit, and mild right hemiparesis (weakness) and continued to recover functionally. At 2.5 years after onset, she was active with minimal aphasia and right-sided vision loss (NIHSS 3). Overall, during the patient’s hospital stay, her hematoma was effectively drained (Figure 1A) and she improved clinically. Midway through her treatment increasing hematoma volume by CT scan and an increase in hematoma drainage suggested that she experienced an asymptomatic re-bleeding event and treatment with tPA was halted (Figure 1A, Supplemental Table 1). Volumetric quantification by serial CT scans suggests that this event occurred between 93 and 105 hours after the onset of the hemorrhage. Increases to the volume and cellularity of hematoma drainage, however, were observed prior to hematoma expansion as measured by imaging, beginning to rise by 73 hours after onset (Figure 1B, Supplemental Table 1). These data suggest that the patient’s re-bleeding event occurred prior to the increased volume seen on CT scan. We hypothesized that detailed analysis of the cellular composition of the hematoma effluent in this patient would provide insight into the molecular features of the hematoma microenvironment, as well as her re-bleeding event, and determine whether immune responses are brain tissue-specific. Using Seq- Well, we performed scRNA-seq on individual hematoma cells and peripheral blood collected longitudinally during her hospitalization as well as peripheral blood collected after 2.5 years, and control blood from age-matched healthy

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donors (Figure 1C).6 We generated single-cell transcriptional profiles for 31,722 cells spanning seven time points after onset, and these are displayed along with control samples using a t-distributed stochastic neighbor embedding (tSNE) (Figure 1D, Supplemental Figure 1 and 2) along the first two components. Replicates (performed for most time points) clustered together across each time point and have comparable quality control metrics (Supplemental Figure 1). Overall, we found that blood and hematoma-derived cells were transcriptionally distinct at almost all time points, and separated in tSNE space by compartment, suggesting that the local milieu within the hematoma is a major driver of leukocyte transcriptional state and function after ICH. After removing red blood cells (Supplemental Methods), we identified seven major cell types, including neurons, B cells, T cells, monocytes, dendritic cells, NK cells and neutrophils (Figure 1D, Supplemental Figure 3 A, B, and C). We found that monocytes/macrophages and T cells were the most abundant in both peripheral blood and hematoma samples. The other major immune cell types were consistently identified in every sample, were consistent across replicate samples and were comparable in quality (Supplemental Figure 3B and D). We confirmed these findings using separately collected longitudinal samples collected from this patient and analyzed by mass cytometry (Supplemental Figure 4, Supplemental Table 2). Our mass cytometry results further corroborate our scRNA-seq frequencies, showing that T cells and monocytes were the predominant cell types profiled in both blood and hematoma. Finally, in our scRNA-seq data, we found a large cluster of neurons that appeared exclusively in hematoma effluent at high frequency prior to increased hematoma drainage at 66 hours (Figure 1D and E, Supplemental Table 2). These neurons were the sole cluster positive for KIF5A, a neuron-specific kinesin subunit (Supplemental Figure 3C).7,8 To assess whether the leukocyte populations identified in the peripheral blood after ICH differed from baseline, we also generated single-cell transcriptional profiles from the patient’s blood collected 2.5 years post-ICH and from the blood of four age-matched healthy donors (Figure 1D and E). The

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follow-up blood and healthy control blood generally overlap, and may suggest an overall return to baseline in circulating leukocytes in the blood of this patient (Supplemental Figure 2). Bulk RNA-seq analysis on sorted populations of immune cells, including CD4+ T cells and monocytes (Supplemental Figure 5 A and B), from this patient’s blood and hematoma at several time points (Supplemental Table 1), combined with hallmark gene set enrichment analysis (Supplemental Figure 5C), corroborates the substantial transcriptomic variation over time despite little variability in broad immune cell frequencies assessed by flow cytometry (data not shown). In the MISTIE III trial, 2% of patients in the surgical arm had a symptomatic re-bleeding event, however 32% had asymptomatic re-bleeding.5,9 Hematoma effluent volume drainage is an imperfect measurement of rebleeding, as some hematoma cavities have variable connections with the ventricular or subarachnoid space and, at times, volume may increase due to CSF drainage. This event is difficult to identify or predict at the bedside. The implications of re- bleeding on the local immune response and long-term outcomes remain unknown but have an important impact of treatment (i.e., cessation of tPA). We hypothesized that a deeper analysis of the predominant cell types in our scRNA- seq data, including T cells and myeloid cells, could better delineate the compartment-specific response to an asymptomatic re-bleed. To accomplish this, we performed sub-clustering analyses on each population separately (Supplemental Methods). Among myeloid cells (macrophage, monocyte/macrophage, and dendritic cells),10 we found that separation by compartment (i.e., blood versus hematoma) was preserved (Figure 2A) and identified 14 sub-clusters, as well as defining genes for each (Figure 2A, Supplemental Figure 6A, Supplemental Table 4). Three of these (myeloid sub-clusters 2, 4, and 5) were almost exclusively found in hematoma effluent (Figure 2B, Supplemental Figure 6B). We found that the frequency of myeloid sub-clusters shifted over time, with several appearing in either blood or hematoma around the time of re-bleeding (Figure 2B).

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Functional gene enrichment analyses on myeloid sub-cluster defining- genes revealed distinct pathways that were active in each (Figure 2C, remaining clusters in Supplemental Figure 7 and Supplemental Table 6). Blood prior to re-bleeding was predominantly comprised of myeloid sub-cluster 3 at 51 hours (enriched for granzyme signaling and glucocorticoid signaling pathways) and myeloid sub-cluster 12 at 66 hours (enriched for iron homeostasis and several T cell interaction pathways). Blood during the re-bleed as detected by catheter drainage (73-89 hours) was predominantly comprised of myeloid sub-cluster 1, enriched for LXR/RXR activation and IL-10 signaling, and then by myeloid sub- cluster 10, enriched for interferon signaling, at 112 and 137 hours. Sub-clusters unique to control and the patient’s long-term follow-up blood (myeloid sub- clusters 0 and 13, Supplemental Figure 6B) were enriched for autophagy. Overall, sub-clusters predominantly in blood were enriched for a spectrum of pathways involved in innate immune cell functions that changed over time, including TLR signaling, glucocorticoid signaling, EIF2 signaling, and interferon signaling. Hematoma prior to re-bleeding was predominantly comprised of myeloid sub-cluster 4, which was enriched for glycolysis and HIF-1α signaling. We only recovered 12 myeloid cells at 66 hours in hematoma, limiting our resolution at this time point. There was then a time-dependent shift from dominance by myeloid sub-cluster 2 to myeloid sub-cluster 5. Myeloid sub-cluster 2 was enriched for pathways related to myeloid cell extravasation and tissue infiltration, possibly indicating new infiltration after rebleeding, (Figure 2C) and myeloid sub- cluster 5 was enriched for pathways related to tissue repair and senescence including phagosome maturation, FGF signaling, autophagy, and senescence. This shift is consistent with a transition between dominance of pro-inflammatory activation and recruitment to phagocytosis and perhaps anti-inflammatory functions. Taken together, we found alterations in the overall transcriptional states of myeloid cells in both blood and hematoma, and gene set enrichments suggest these cells, particularly in hematoma, may have differing functions altered by their unique physiological niche that shift over time.

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To better contextualize the phenotypes of myeloid sub-clusters, we performed module scoring against a list of curated human monocyte/macrophage gene sets (Supplemental Table 6, Supplemental Figure 8A).11-14 We performed clustering and principal component analysis (PCA) on these scores to identify modules that explain differences across myeloid clusters. We found that most of these known signatures did not explain differences between myeloid sub- clusters (Supplemental Figure 8A). Nonetheless, we identified four gene signatures that predominantly explain differences between myeloid sub-clusters that were either hematoma or blood in origin, well-describing blood monocytes/macrophages but not those from hematoma (Supplemental Figure 8B). Specifically, we found that the predominantly blood sub-clusters (myeloid sub-clusters 0, 1, 3, 6, 7-13) and the sub-cluster that emerged in the hematoma at 73 hours (myeloid sub-cluster 2) scored similarly for a CD14+ monocyte gene signature identified recently in blood12 (Figure 2D, Supplemental Figure 9A). Taken together, our data suggest that myeloid sub-cluster 2 may represent monocytes newly entered into the hematoma due to a recent re-bleed, while other hematoma clusters, like myeloid clusters 4 and 5, are defined by activation states induced by local signals in the hematoma and do not resemble clusters previously defined in human blood or by in vitro stimulations.12-14 When considered in the context of the timing of the increased draining volume from the catheter and the emergence of a neuron-like cluster at 66hrs, our data may suggest a re-bleeding event prior to the clinically identified changes in hematoma volume detected by a daily CT scan. Our data also revealed substantial involvement of T cells in the hematoma at all time points. Sub-clustering over all hematoma and peripheral blood T cells from the patient and control blood resulted in 12 T cell sub-clusters that also separated overall by compartment (Figure 3A, Supplemental Figure 9). As with monocytes, we found that T cell frequencies were dynamic over the course of ICH in hematoma (Supplemental Table 7). In the blood, there was an initial shift in the dominant cluster from T cell sub-cluster 3 to T cell sub-cluster 2 prior to the re-bleed, which then remained highly frequent for the remaining time points

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(Figure 3B). Several sub-clusters were also predominantly blood in origin (sub- clusters 0, 2, 4, 10, and 11). T cell sub-clusters 2 and 3, which were enriched for IL-17A and GADD45 signaling, respectively, also emerged in the hematoma at 73 hours. The emergence of these clusters in hematoma at this time paralleled the emergence of a more blood-like monocyte sub-cluster at the same time point, further supporting evidence this was the beginning of the rebleeding. Several sub-clusters of T cells were predominantly found in hematoma (Supplemental Figure 9B) (e.g., T cell sub-clusters 1, 3, 5, 6, and 8). T cell sub- cluster 5, prevalent in early hematoma samples, was enriched for heat shock proteins, protein ubiquitination, and eNOS signaling (Figure 3D, Supplemental Table 8). T cell sub-cluster 3, which emerged at 73 hours, was enriched in GADD45 signaling and several immune activation pathways (Figure 3C, additional clusters in Supplemental Figure 10). T cell sub-cluster 1, emerging at later time points in hematoma, shows expression of genes involved in transcriptional regulation, anti-proliferation, and iron homeostasis. Interestingly, we did not find clear patterns of chemokine and receptor expression15 in specific clusters in either myeloid or T cell sub-clusters; we did find, however, elevated expression of CXCR4 consistently across myeloid and T cell sub-clusters (Supplemental Figure 11). Overall, we found that T cells that originated in blood are enriched for innate immune interactions, IL-17 signaling, chemokine signaling and sirtuin signaling, whereas those predominantly from hematoma are enriched for stress response pathways, diapedesis and neuroinflammation. Both the patient’s long-term follow up blood sample and the healthy control blood samples were predominated by T cell sub-cluster 0, which was defined by two marker genes (TXNIP and LTB). To better contextualize our T cell sub-clustering results, we again performed module-scoring analysis with known T cell gene signatures previously derived from human transcriptional data (Supplemental Table 10). Unsupervised clustering followed by PCA on module scores revealed several modules that describe variation across T cell sub-clusters (Supplemental Figure 12A). We found that three gene modules that describe effector functions in T

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cells drove major axes of variation in our dataset (Figure 3D). One signature is associated predominantly with T cell sub-cluster 4 and is defined by T cell effector genes like GNLY, CCL4, and GZMK (Effector Module Score 1, Supplemental Figure 12B). The other is associated with T cell sub-cluster 6, which is a hematoma cluster emerging during rebleeding, and is defined by T cell effector genes like CXCR4, TXN, and CSTB (Effector Module Score 2, Figure 3d, Supplemental Figure 12B). Generally, cells in clusters on the right in the tSNE plot shown in Figure 3D, including T cell sub-clusters 0, 2, and 7 (found predominantly in blood), score higher for a naïve T cell module. Overall, these data – combined with our pathway enrichment results – suggest that T cells in the predominantly hematoma-derived clusters may be enriched for unique T cell functions, including several related to effector functions and stress responses. Based on the emergence of several blood-like monocyte and T cell sub- clusters in the hematoma at 73 hours, as well as increased drainage volume and cellularity, we estimate that the time of re-bleeding was between 66 and 73 hours after ICH onset, a full day prior to detection by CT scan. It is also intriguing that neurons emerged in the hematoma effluent immediately prior to this event, but the consequences of this is unknown as the patient continued to improve. Monitoring of specific peripheral blood biomarkers found in effluent could thus potentially enable earlier detection of a re-bleeding event to guide a more effective treatment paradigm. We caution, however, that our study presents only one patient’s ICH trajectory; nevertheless, it suggests that larger scale scRNA- seq studies could provide more insight into specific blood cell states that may better inform patient treatment and outcome and highlight pathways for future research. Our transcriptional analyses of leukocytes over the course of ICH in this patient suggest coordinated, dynamic stages of monocyte and T cell responses within the hematoma that were altered after asymptomatic re-bleed, indicating rapid adaptation and response to local changes in the hematoma tissue milieu. Additionally, we studied the cellular composition of the blood of this patient more than two years after ICH, and our data suggest that the peripheral blood of this

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patient, who had an excellent outcome, is comparable to that of age-matched controls. The dynamics of leukocyte infiltration and activation after ICH have been difficult to study in patients, especially at the site of injury.16 At present, our understanding is derived primarily from rodent models and peripheral measures; how the kinetics in these models compares to those in humans is unknown.17,18 Using the infrastructure of the MISTIE III trial, we characterized the local cellular response to ICH and an associated re-bleeding event in the brain of a living patient at single-cell resolution for the first time. We characterized thousands of single cells from both blood and hematoma effluent at several points after ICH onset, allowing us to identify cellular phenotypes that were dynamic in both the T cell and myeloid lineages. Most notably, the emergence of myeloid sub-cluster 2 and T cell sub-cluster 2 in the hematoma after re-bleeding, followed by the sequential emergence of new cell states, suggests that newly infiltrating immune cells adapt transcriptionally to dynamic injury events in the brain. Crucially, our work demonstrates how rapidly immune cell activation states change after brain injury in humans and sets the stage for framing critical time periods for future studies. Our data, while we caution is a small case study, provide proof-of-principle that, in the near future as personalized medicine evolves, this and other approaches could be used clinically to interrogate cellular responses and uncover meaningful clinical events in patients to help guide treatment decisions.

METHODS The methods are described in the Supplemental Materials.

Study approval. The patient was enrolled in the MISTIEIII trial (NCT01827046) under an IRB approved protocol including sample collection. Control donors (n=4, 2 males and 2 females, ages 63-93) were recruited and samples were also collected under an IRB approved protocol.

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AUTHOR CONTRIBUTIONS LHS designed the study in consultation with DFH, JCL, BAG, MHA and AKS. LHS, AKS, and JCL supervised the study at all stages. Sample processing was performed by MHA and HEB. BAG, MHA, JHD, NHP performed Seq-Well and associated sequencing with assistance from IF, EM and RSD. BAG performed transcriptional analysis with input from LHS, MHA, JCL and AKS. MHA performed mass cytometry and mass cytometry analysis. DFH, IAA, and MZ led the MISTIE III trial internationally and provided trial data for this study and CCM and LHS led trial at Yale and provided patient samples and additional data. BAG, MHA, JCL, AKS, and LHS wrote the manuscript with critical input from all authors.

ACKNOWLEDGEMENTS MISTIEIII (U01NS080824, PI Hanley), Trial innovation Network (U01NS080824 PI Hanley) and JHU CTSA (U24TR001609 Hanley section PI), ICHseq (R01NS097728, PI Sansing), NRSA postdoctoral fellowship (F32-AI136459, Goods), AHA postdoctoral fellowship (17POST33660872, Askenase), Swanson Biotechnology Center at the Koch, Searle Scholars Program (Shalek), the Beckman Young Investigator Program (Shalek), a Sloan Fellowship in Chemistry (Shalek), and NIH 5U24AI118672 (Shalek).

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FIGURES

Figure 1. scRNA-seq on cells isolated from hematoma effluent and peripheral blood in a living patient over the course of ICH. A. Selected CT scans as a function of time after onset (hrs). The time of catheter placement is indicated. B. Plot of hematoma volume and drainage as a function of time as

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measured by both CT scan and the volume of hematoma effluent from the catheter in the eight hours prior to collection. C. Schematic overview of sample collection and processing for generating scRNA-seq data from patient hematoma effluent and blood. Single cells from hematoma effluent and blood were isolated, and scRNA-seq profiles were generated using the Seq-Well platform. D. t- distributed stochastic neighbor embedding (tSNE) plot along components 1 and 2 for all high-quality single cells (n=24,877 single cells across seven patient time points and n=6,845 single cells across four control donors). The tSNE plot is colored by compartment of origin (left), time after onset (middle), or cell identity (right). Cell identity was determined by SNN clustering, marker selection, and module scoring (Supplemental Figure 2, Supplemental Methods). The estimated onset of the asymptomatic re-bleeding event is indicated with a black bar and is based on changes detected in CT scan, catheter drainage data, and sequencing data. E. Stacked frequency plots for each indicated condition and colored by identified cell type. Estimated earliest time point of re-bleeding event is indicated. Total cell counts per cluster are reported directly in Supplemental Table 3.

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Figure 2. Shifts in prevalence and phenotypes of myeloid cells in hematoma effluent and blood over time. A. tSNE plot of re-clustered monocytes, macrophages, and dendritic cells from Figure 1 (n=8,292 cells). The re-clustered tSNE plot is colored by hematoma or blood (left), time after onset (middle), or by sub-cluster 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 sub-clusters 4, 2, 5 emerge sequentially in hematoma, Myeloid sub-clusters 3, 1, and 10 emerge sequentially in blood, and sub-cluster 0 predominates in patient follow-up at 2.5 years and control blood. Remaining clusters are presented in Supplemental Figure 8. Pathways with three or more molecules in the query gene list are bolded. D. Violin plot of module scores for an inflammatory monocyte gene signature for each sub- cluster. All gene modules scored are presented in Supplemental Figure 9. Each sub-cluster is annotated as predominantly blood (red +) or hematoma (grey +) in

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origin below the plot. Annotated adjusted p values were calculated using Wilcoxon 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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Figure 3. Shifts in prevalence and phenotypes of T cells in hematoma effluent and blood over time. A. tSNE plot showing re-clustered T cells (n=16,883 cells) from Figure 1. The re-clustered tSNE plot is colored by hematoma or blood (left), time after onset (middle), or T cell sub-cluster identity (right) determined by re-clustering analysis. B. Stacked frequency plot of new T cell clusters by hours after onset in blood and hematoma. C. Top 10 significantly enriched IPA pathways for selected clusters. T cell sub-clusters 5, 3, and 1 emerge in hematoma and T cell sub-clusters 4, 2, and 7 are found in blood. Remaining clusters are presented in Supplemental Figure 11. Sub-cluster 0 was defined by two marker genes (TXNIP and LTB). Pathways with three or more molecules in the query gene list are bolded. D. tSNE plots colored by each indicated module score.

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REFERENCES

1. Mozaffarian, D. et al. Heart Disease and Stroke Statistics—2016 Update. Circulation 133, 1–8 (2016). 2. Krishnamurthi, R. V. et al. Articles Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. The Lancet Global Health 1, e259– e281 (2013). 3. Wu, G. et al. Post-operative re-bleeding in patients with hypertensive ICH is closely associated with the CT blend sign. 1–8 (2017). doi:10.1186/s12883-017-0910-6 4. Ziai, W., Carhuapoma, J., Nyquist, P. & Hanley, D. Surgical Strategies for Spontaneous Intracerebral Hemorrhage. Semin Neurol 36, 531–541 (2016). 5. Hanley, D. F. et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. The Lancet 393, 1021–1032 (2019). 6. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nature Methods 1–8 (2017). doi:10.1038/nmeth.4179 7. Fagerberg, L. et al. Analysis of the Human Tissue-specific Expression by Genome-wide Integration of Transcriptomics and Antibody-based Proteomics. Mol Cell Proteomics 13, 397–406 (2014). 8. Nakajima, K. et al. Molecular Motor KIF5A Is Essential for GABAA Receptor Transport, and KIF5A Deletion Causes Epilepsy. Neuron 76, 945–961 (2012). 9. Hanley, D. F. et al. Safety and efficacy of minimally invasive surgery plus alteplase in intracerebral haemorrhage evacuation (MISTIE): a randomised, controlled, open-label, phase 2 trial. The Lancet Neurology 15, 1228–1237 (2016). 10. Tang-Huau, T.-L. et al. Human in vivo-generated monocyte-derived dendritic cells and macrophages cross-present antigens through a vacuolar pathway. Nature Communications 1–12 (2018). doi:10.1038/s41467-018- 04985-0 11. Martinez, F. & Gordon, S. The M1 and M2 paradigm of macrophage activation: time for reassessment. 1–13 (2014). doi:10.12703/P6-13) 12. Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573–14 (2017). 13. Shay, T. & Kang, J. Immunological Genome Project and systems immunology. Trends in Immunology 34, 602–609 (2013). 14. Xue, J. et al. Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation. Immunity 40, 274–288 (2014). 15. Ramesh, G., MacLean, A. G. & Philipp, M. T. Cytokines and Chemokines at the Crossroads of Neuroinflammation, Neurodegeneration, and

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Neuropathic Pain. Mediators of Inflammation 2013, 1–20 (2013). 16. Thelin, E. P. et al. Monitoring the Neuroinflammatory Response Following Acute Brain Injury. Front. Neurol. 8, 231–14 (2017). 17. Tsai, A. S. et al. A year-long immune profile of the systemic response in acute stroke survivors. Brain 142, 978–991 (2019). 18. Durocher, M. et al. Inflammatory, regulatory, and autophagy co-expression modules and hub genes underlie the peripheral immune response to human intracerebral hemorrhage. 1–21 (2019). doi:10.1186/s12974-019- 1433-4

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Supplemental Appendix

Table of Contents

Author Lists………………………………………………….…..2-3 Supplemental Methods ………………………………………. 4-9 Supplemental Figures …………………………………..……..10-28 Supplemental Tables …………………………………….…….28-47 Supplemental References……………………………………..48

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Brittany A. Goods, PhD1,2*, Michael H. Askenase, PhD3*, Erica Markarian4, Hannah E. Beatty3, Riley Drake1, Ira Fleming1, Jonathan H. DeLong3, Naomi H. Philip5, Charles C. Matouk, MD6, Issam A. Awad, MD7, Mario Zuccarello, MD8, Daniel F. Hanley, MD9, J. Christopher Love, PhD2,4,10,#, Alex K. Shalek, PhD1,2,4,11,12,13,14,#, and Lauren H. Sansing, MD, MS3,15,#, on behalf of the ICHseq Investigators

1Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology 2Broad Institute, Harvard University & Massachusetts Institute of Technology 3Department of Neurology, Yale University School of Medicine 4Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology 5Department of Immunobiology, Yale University School of Medicine 6Department of Neurosurgery, Yale University School of Medicine 7Department of Neurosurgery, University of Chicago 8Department of Neurosurgery, University of Cincinnati 9Brain Injury Outcomes Division, Johns Hopkins School of Medicine 10Department of Chemical Engineering, Massachusetts Institute of Technology 11Ragon Institute, Harvard University, Massachusetts Institute of Technology, & Massachusetts General Hospital 12Division of Health Science & Technology, Harvard Medical School 13Program in Computational & Systems Biology, Massachusetts Institute of Technology 14Department of Immunology, Massachusetts General Hospital 15 Human and Translational Immunology Program, Yale School of Medicine *equal #equal

Corresponding Authors: Lauren Sansing, [email protected], Alex K. Shalek, [email protected], J. Christopher Love, [email protected]

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ICHSeq Investigators Site Principal Investigator Lead Coordinator Abington Larami MacKenzie Karin Jonczak Barrow Peter Nakaji Norissa Honea Duke Michael James Erlinda Yeh Hennepin Thomas Bergman Kathy France INOVA James Leiphart Tricia Brannan Johns Hopkins Wendy Ziai Vikram Madan Kansas U Medical Center Paul Camarata Jason Gorup Mayo Jacksonville Ronald Reimer Emily Edwards Miami Valley John Terry Angie Shoen Northwestern Babak S. Jahromi Byron Yip Rush George Lopez Becky Holtz Rutgers Igor Rybinnik Michelle Moccio St Lukes Darren Lovick Kelsey Titus Thomas Jefferson Jack Jallo Kara Pigott U Alabama Birmingham Mark Harrigan Lisa Nelson University of Michigan Ventatakrishna Rajajee/Aditya Ron Ball Pandey U New Mexico Andrew Carlson Amal Alchbli U Louisville Robert F. James Ann Jerde U Utah Safdar Ansari Joshua Letsinger Yale Kevin Sheth/Lauren Sansing Linda Ryall BIOS and MTI:M3 Investigators Johns Hopkins Paul Nyquist Mass General Josh Goldstein Johns Hopkins Vikram Madam Johns Hopkins Christina Grabartis Johns Hopkins Tracey Economas Johns Hopkins Wendy Ziai Manchester Adrian R. Parry-Jones

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Supplemental Methods

Patient enrollment The patient was enrolled in the MISTIEIII trial (NCT01827046) under an IRB approved protocol including sample collection. Control donors (n=4, 2 males and 2 females, ages 63-93) were recruited and samples were also collected under a IRB approved protocol.

Patient hematoma effluent and blood collection and processing Each sample was centrifuged at 500g for 10 minutes and the supernatant was removed. Then each sample was resuspended in HBSS (Gibco) and treated with 2.5 Units/ml of Benzonase (Sigma #E1014) for 10 minutes at room temperature. Samples were then passed through a 70-micron filter to remove tissue debris, washed with 25 ml of HBSS, and centrifuged at 300g for 10 minutes. After supernatant was removed, platelets and most erythrocytes were removed from these samples using a LeukoLock filter (Life Technologies) as previously described. Briefly, after the sample passed through the filter binding leukocytes, the filter was backflushed to recapture the leukocytes. This backflush was centrifuged at 300g for 8 minutes and supernatants were removed until the mixed leukocyte/erythrocyte cell pellet composed approximately 50% of the total volume of the sample. Granulocytes were removed from this sample using the RosetteSep Human Granulocyte Depletion Cocktail (Stem Cell Technologies) and subsequent Ficoll density gradient centrifugation according to the manufacturer’s protocol. Finally, the resulting cell pellets, which still contained trace numbers of erythrocytes, were subjected to two rounds of erythrocyte lysis at room temperature for 5 minutes each using BD PharmLyse buffer, washed, and resuspended in RPMI containing 10% FBS.

Generation of single-cell RNA-sequencing (scRNA-seq) data with Seq-Well Seq-Well was performed as described previously.1 About 15,000 cells were loaded onto each array preloaded with uniquely-barcoded mRNA capture beads (ChemGenes). Arrays were washed with protein-free RPMI media, then sealed with polycarbonate membranes. Arrays were incubated at 37°C for 30 minutes to allow membranes to seal,

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then transferred through a series of buffer exchanges to allow for cell lysis, transcript hybridization, bead washing, and bead recovery from arrays post membrane removal. Reverse transcription was performed with Maxima H Minus Reverse Transcriptase (ThermoFisher), excess primers were removed using an Exonuclease I digestion (New England Biolabs), and whole transcriptome amplification (WTA) by PCR was performed using KAPA Hifi PCR Mastermix (Kapa Biosystems). WTA product was purified using Agencourt Ampure beads (Beckman Coulter) and 3’ digital gene expression (DGE) sequencing libraries were prepared using Nextera XT (Illumina). Two arrays worth of sequencing libraries were sequenced per NextSeq500/550 run using a 75 cycle v2 sequencing kit (Illumina) with a paired end read structure (R1: 20 bases; I: 8 bases; R2: 50 bases) and custom sequencing primers.

scRNA-seq computational analyses Raw sequencing data was demultiplexed and aligned to the Hg19 genome using publicly available scripts on Terra (scCloud/dropseq_workflow, version 11, https://app.terra.bio/). The resulting UMI-collapsed digital gene expression matrices were used as input to Seurat (v3) for further analyses in R (v3.6.2). Initial clustering was performed, and on the basis of marker genes identified using Seurat’s Wilcoxon rank- sum test, red blood cells were identified by expression of hemoglobin genes and excluded from further analysis, resulting in a total of 31,722 cells across all conditions. All data were normalized, the top 2,000 variable genes identified, and the data was clustered at a resolution of 2. Marker genes for each cluster were identified using Seurat’s Wilcoxon rank-sum test, and broad immune cell types were labeled by comparing to known marker genes that have been previously published, and clusters were collapsed into labels as shown in Figure 1. 2,3

For sub-clustering analyses, T cells or myeloid cells were analyzed separately. Each cell-type was re-normalized, the top 2,000 variable genes were identified, and the data was clustered across several resolutions to identify resolutions that produced non- redundant clusters (resolution = 0.6 for T cell sub-clustering, and resolution = 0.6 for

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myeloid sub-clustering) as determined by marker gene identification using Seurat’s Wilcoxon rank-sum test. Notebooks to reproduce all analyses performed in R will be available for download (https://github.com/ShalekLab).

Functional enrichment was performed using Ingenuity Pathway Analysis (Qiagen, Version 47547484) using adjusted p-values of significant genes in each cluster as input, and significant canonical pathways were reported if their p-value was significant

(padj>0.05). If three or more molecules of the pathways were identified in the input gene lists, these pathways are bolded in the main manuscript figures. For creating gene module scores, gene lists were manually curated from existing literature sources (Supplemental Table 6 and 10) and scores were generated in R using Seurat.

Mass cytometry sample preparation Cell suspensions from each time point was stained for mass cytometry and fixed immediately upon isolation.4 Antibody mixes (Supplemental Table 2) were prepared fresh each day in BSA stain buffer (BD, #554657) and centrifuged through a 0.1 micron filter (Millipore #UFC30VV00) at 12,000g for 4 minutes; this process reduced non- specific labeling of cells with free isotopes. Up to 3 x 106 cells in RPMI containing 10% FBS were transferred to a 1.5 ml Eppendorf tube and washed with phosphate-buffered saline (PBS). Cell suspensions were centrifuged at 300g for 8 minutes and supernatant was removed from the cell pellet. For all centrifugation steps, a fixed rotor micro- centrifuge was used and supernatants were carefully removed by pipette. To mark cells for viability, cell pellets were resuspended in 1 µM Cell-ID cisplatin (Fluidigm #201194) and incubated for 3 minutes at room temperature. Cells were washed with 1 mL of BSA stain buffer, centrifuged at 300g for 8 minutes, and supernatant was carefully removed. Cell pellets were resuspended in antibody mix and incubated on ice for 40 minutes. Cell pellets were washed with 1 mL of BSA stain buffer, centrifuged, resuspended in Fix/Perm buffer from the FoxP3 staining kit (eBiosciences, #00-5523-00), and incubated overnight at 4°C. The next morning, samples were centrifuged at 800g for 5 minutes, resuspended in 300 µL BSA stain buffer, and stored at 4°C until all timepoints had been collected. Then, all samples were centrifuged and resuspended in 125 nM Cell-ID

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Intercalator-IR (Fluidigm #201192A) diluted in Fix/Perm buffer, and incubated overnight at 4°C. Immediately before running each sample on the mass cytometer, that sample was centrifuged, supernatant was removed, and the pellet was resuspended in deionized water.

Data analysis for mass cytometry Initial gating was performed to identify CD45hi cisplatinlo viable leukocytes, after first using Iridium intercalator to exclude doublets and non-cellular debris. Cells meeting these criteria were then normalized using bead standards to account for variations in cytometer sensitivity as previously described.4 Cell populations were identified using FlowJo software (v10, Treestar) according to the following marker combinations for each: CD4 T cells (CD3+CD19-CD4+), CD8 T cells (CD3+CD19-CD8a+), NK cells (CD3- CD56+CD11blo), B cells (CD3-CD56-CD11b-CD19+), monocytes (CD3-CD56- CD11bhiCD19-CD66a-CD14+CD11c+), neutrophils (CD3-CD56-CD11bhiCD19- CD66a+HLA-DRlo).

Bulk RNA-sequencing sample preparation and analysis Up to 5 x 106 leukocytes isolated from each sample by the above method were used for downstream sorting. Cells were first lightly fixed in 1 mL of Cell Cover, a light preservative that maintains RNA integrity (Anacyte #800-250), on ice for 10 minutes, then centrifuged. CD3+ cells were separated from total cell suspensions by magnetic selection (StemCell, #17851) according to manufacturer’s instructions. CD3+ cells were stained for flow cytometry using CD45 (Tonbo, #35-0459-T100), CD25 (BD Biosciences, #555432), CD8 (Tonbo, #60-0088-T100), CD127 (Biolegend, #351316), CD2 (BD Biosciences, #562638), CD4 (Tonbo, #75-0049-T100), CD11b (Tonbo 30- 0112-U500), CD20 (Biolegend, #302349), CD56 (Biolegend, #318320), and viability dye (Life Technologies, #L34972). CD45hi CD11b– CD56– CD20– CD2+ CD4+ CD8–, CD127hi CD25– live cells were sorted for CD4 T cell library construction. CD3– cell fractions were stained for flow cytometry with CD45 (Tonbo Biosciences, #50-0459-T100), CD11b (Tonbo, #35-0118-T100), CD14 (Biolegend, #301820), CD16 (BD Biosciences, #560474), CD66a/c/e (Biolegend, #342310), CD2, (Biolegend, #300204), CD20

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(Biolegend, #302349), CD56 (Biolegend, #318320), and viability dye. CD45hi CD2– CD56– CD20– CD66– CD11bhi CD14hi CD16lo cells were sorted for monocyte library construction. Monocyte and CD4 populations were sorted using a FACSaria II (Supplemental Figure 7A and B) directly into RNA lysis buffer composed of 200 µL RA1 buffer (Macherey-Nagel, #RA1) freshly spiked with 2% TCEP (Thermofisher, #77720).

RNA-sequencing libraries were generated as previously described.5 Briefly, RNA was extracted using the NucleoSpin RNA XS Kit (Macherey-Nagel, #740902) according to the manufacturer’s instructions. Smart-Seq2 cDNA synthesis was performed with the following modifications: 1) input RNA was normalized prior to cDNA generation by diluting to ~1,000 cells per reaction, 2) reverse transcription was performed with Superscript III (ThermoFisher, #18080-085) in place of Superscript II according to the manufacturer instructions. Paired-end sequencing libraries were prepared using the Nextera XT DNA sample Prep Kit (Illumina, #FC-131) according to the manufacturer’s instructions. Libraries were pooled in an equimolar ratio and sequenced on a NextSeq500/550 sequencer (Illumina) using a 75 cycle v2 sequencing kit with a paired end read structure. Following sequencing, BAM files were converted to merged, demultiplexed FASTQs. Paired-end reads were mapped to the UCSC hg19 genome using STAR and RSEM. SSGSEA was performed on the Hallmark gene sets using the “SSGSEAProjection” module version 9.1.1 on the GenePattern website (https://www.genepattern.org/) with the following parameters: weighting exponent = 0.75, minimum gene set size = 10, and combine mode = combine.add. Heatmaps of resulting ssGSEA results were generated using Morpheus.

Flow cytometry analysis of blood and hematoma leukocyte populations Cell suspensions were generated as described above, except granulocytes were not depleted prior to red blood cell lysis. 106 whole blood leukocytes from the resulting cell suspensions were stained for flow cytometry with CD14 (eBioscience, 11-0149-42), CD25 (BD Biosciences, 555432), CD66a/c/e (Biolegend, 342310), CD45 (Biolegend, 304012), CD3 (BD Biosciences, 560176), CD4 (Tonbo, 75-0049-T100), CD8 (BD

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Biosciences, 560774), CD11b (Biolegend, 301336), and viability dye (Life Technologies, L34972). Flow cytometry was performed on a BD Fortessa (Supplemental Figure 5A). After eliminating dead cells using viability dye, the following populations were identified using FlowJo software (Treestar): CD4 T cells (SSClo, CD45+, CD66–, CD3+, CD11b–, CD4+, CD8–), CD8 T cells (SSClo, CD45+, CD66–, CD3+, CD11b–, CD8+, CD4–), CD14hi monocytes (SSClo, CD45+, CD66–, CD11b+, CD3–, CD14hi, CD16lo), CD16hi monocytes (SSClo, CD45+, CD66–, CD11b+, CD3–, CD16hi, CD14lo), and neutrophils (SSChi, CD45lo, CD66+, CD16+).

Statistical analysis Statistical analyses for comparing cell frequencies obtained with CyTOF or Seq-Well were performed using Prism (8.4.3). P values were calculated by repeated measures analysis of variance (ANOVA) followed by post hoc test (Kruskal-Wallis, non- parametric) and also where indicated standard error bars are shown. For comparison of module scores, analyses were performed in R (v3.6.2). Module scores between myeloid sub-clusters were compared by Kruskal-Wallis rank sum test followed by pairwise comparisons using Wilcoxon rank sum test with Benjamini & Hochberg p value correction.

Data availability Raw sequencing data will be made available through Gene Expression Omnibus (Study ID: #####) concurrent with publication, as well as through The Alexandria Project (https://singlecell.broadinstitute.org/single_cell?scpbr=the-alexandria-project).

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Supplemental Figures

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Supplemental Figure 1. Reproducibility across replicates. tSNEs of data for each time point post ICH are shown and colored by metadata (compartment or sequencing platform) and replicatearray. Violin plots of quality control metrics including number of unique molecular identifiers (nUMIs), number of genes detected (Genes), and fraction of mitochondrial genes for each SeqWell array.

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Supplemental Figure 2. tSNE of all data shown by hematoma (top), blood (middle), or control and patient 2.5 year follow-up. tSNEs are also shown by individual time point, showing cell yield for each array.

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Supplemental Figure 3. tSNE of all data clustered including all cell clusters with key cell type identification genes as feature plots. A. tSNE colored by compartment, time, and cell type reproduced from Figure 1. B. Violin plots of quality control metrics including number of unique molecular identifiers (nUMIs), number of genes detected

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(Genes), and fraction of mitochondrial genes per cell plotted by cell type. C. Violin plots show increased expression of selected marker genes for each cluster. D. Frequency of each cell type is shown for replicate arrays.

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Supplemental Figure 4. Mass cytometry analysis of hematoma and blood. A. Stacked bar chart of percent of live cells. Cell populations were identified according to the following marker combinations for each: CD4 T cells (CD3+CD19-CD4+), CD8 T cells (CD3+CD19-CD8a+), NK cells (CD3– CD56+CD11blo), B cells (CD3–CD56–CD11b– CD19+), monocytes (CD3–CD56– CD11bhiCD19–CD66a–CD14+CD11c+), neutrophils (CD3–CD56–CD11bhiCD19– CD66a+HLA-DRlo). B. Stacked bar charts of indicated T cell subsets. C. Comparison of cell type frequencies between indicated platform (CyTOF or Seq-Well).

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Supplemental Figure 5. Patient bulk RNA-seq. A. Representative sorting strategy for CD4+ T cells isolated from blood and hematoma for bulk RNA-seq. B. Representative sorting strategy for monocytes isolated from blood and hematoma for bulk RNA-seq. C. Heatmaps of SSGSEA results from bulk RNA-sequencing on monocytes and CD4+ T cells from blood or hematoma for non-overlapping time points from this patient.

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Supplemental Figure 6. Myeloid re-clustering analysis. A. Heatmap of top ten differentially expressed genes for each myeloid sub-cluster. B. Stacked bar chart showing frequency of compartment (blood or hematoma) for each myeloid sub- cluster. C. Violin plots of selected marker genes for each myeloid sub-cluster.

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Supplemental Figure 7. Waterfall plots of top 10 enriched pathways by IPA for remaining new myeloid clusters. Results, including genes in each pathway, are also shown in Table S5.

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Supplemental Figure 8. A. Gene expression module scores were calculated for selected gene modules from the literature for each cell in our myeloid data and clustered hierarchically. The heatmap is annotated by compartment (blood or hematoma), time, source (control or patient), and myeloid sub-cluster. Each module scored over is annotated by primary stimulation condition. B. Principal component analysis was performed on all module scores. Data are shown across the first two principal components and all module variable loadings are projected onto the top left plot and each subsequent plot shows only the top four module loading (black arrows). Data from each myeloid sub-cluster is visualized

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separately (sub-clusters 0-14), or as a function of time in either blood or hematoma, and as patient derived or control blood derived. Plots are grouped by metadata characteristics, predominantly blood, predominantly hematoma, or both. Gene signatures used for module scoring are described in Table S6.

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Supplemental Figure 9. T cell re-clustering analysis. A. Heatmap of top ten differentially expressed genes for each T cell sub-cluster. B. Stacked bar chart of all T cell sub-clusters by blood or hematoma. C. Violin plots of selected marker genes for each T cell sub-cluster. D. tSNEs of re-clustered T cell data colored by PTPRC, CD8A, CD4, and CCR7 expression.

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Supplemental Figure 10. Waterfall plots of top 10 enriched pathways by IPA for remaining T cell sub-clusters. Results, including genes in each pathway, are also shown in Table S9.

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Supplemental Figure 11. Dot plots of canonical ligands and receptors for A. myeloid sub-clusters, and B. T cell sub- clusters.

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Supplemental Figure 12. A. Gene expression module scores were calculated for selected gene modules from the literature for each cell in our myeloid data and clustered hierarchically. The heatmap is annotated by compartment (blood or hematoma), time, source (control or patient), and T cell sub-cluster. B. Principal component analysis was performed on all module scores. Data are shown across the first two principal components and all module variable loadings are projected onto the top left plot and each subsequent plot shows only the top three module loading (black arrows). Data

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from each myeloid sub-cluster is visualized separately (sub-clusters 0-11), or as a function of time in either blood or hematoma, and as patient derived or control blood derived. Plots are grouped by metadata characteristics, predominantly blood, predominantly hematoma, or both. Gene signatures used for module scoring are described in Table S10.

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Supplemental Tables

Table S1. Patient time course.

Hrs after onset Hematoma Hematoma Cell Count Cells Sample Collection and Surgical Event and/or Scan Volume Volume on (x10^6) (x10^6) / Study Event from CT scan mL Catheter (mL) (mL)

0 time of onset 35.5 85.7 scan 50.9 45 2.3 0.12 SeqWell (9 mL), sort placement of catheter and removal of liquid hematoma 52.3 36.6 scan 56.4 catheter pulled back 1.5cm 63.6 33 scan 66 11 0.4 0.09 SeqWell (4.5 mL) sample collection followed by administration of tPA (dose 1) 73.2 38 4.7 0.36 SeqWell (13 mL), sample collection followed by CyTOF administration of tPA (dose 2) 78.2 26.5 scan 80.9 53 Sort administration of tPA (dose 3) 89 30 14 0.39 SeqWell (30 mL) Sample collection followed by administration of tPA (dose 4) 93.4 25.9 scan 97.6 33 1 CyTOF 105.4 30.2 Sort scan 112 11 7.8 2.6 SeqWell (9 mL), CyTOF 115 31.4 scan 121 13 2.7 130.8 12.8 Sort Scan

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137 4 2.2 0.63 SeqWell (3.5 mL) 140.1 Catheter removal 165.1 11.8 scan

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Table S2. CyTOF antibodies.

Isotope Epitope Fluidigm Catalog # 89Y CD45 3089003B 142Nd CD19 3142001B

145Nd CD4 3145001B 146Nd CD8 alpha 3146001B 147Sm CD11c 3147008B 149Sm CD66a 3149008B 151Eu CD14 3151009B 167Er CD11b 3167011B

170Er CD3 3170001B 174Yb HLA-DR 3174001B 176Yb CD56 3176008B

191Ir DNA (Singlets) 201192A 193Ir DNA (Singlets) 201192A 194Pt Live/Dead 201194

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Table S3. scRNA-seq cell counts shown in Figure 1.

Condition (Compartment, B cells CD1C+ CD8 T granulocy macrophag monocyte/mac neurons NK cells T cells TOTAL Time) DCs cells tes e rophage Blood, 51 hrs 33 72 251 27 20 834 0 62 483 1782 Blood, 66 hrs 18 10 0 15 13 181 0 11 57 305 Blood, 73 hrs 38 4 100 7 36 323 0 278 664 1450 Blood, 89 hrs 57 22 110 20 198 872 0 209 1543 3031 Blood, 112 hrs 49 26 147 13 40 158 0 358 1590 2381 Blood, 137 hrs 9 13 10 13 61 165 0 59 484 814 Blood, patient followup 133 88 70 20 1 327 0 197 794 1630 Blood, control 00 67 13 77 2 0 47 0 425 1184 1815 Blood, control 01 35 74 29 27 9 771 0 150 518 1613 Blood, control 02 10 60 4 15 5 1531 0 21 151 1797 Blood, control 03 56 56 192 9 0 313 0 238 756 1620 Hematoma, 51 hrs 13 2 9 1 425 0 17 18 1126 1611 Hematoma, 66 hrs 2 0 0 0 12 0 351 6 248 619 Hematoma, 73 hrs 91 55 200 20 816 39 1 278 2145 3645 Hematoma, 89 hrs 102 9 20 9 121 3 0 171 1422 1857 Hematoma, 112 hrs 83 17 12 6 235 13 8 601 2336 3311 Hematoma, 137 hrs 32 4 1 2 196 2 7 307 1890 2441

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Table S4. Myeloid cell re-clustering analysis cluster membership cell counts.

Condition Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- Sub- (Compartment, cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster cluster TOT Time) 0 1 10 11 12 13 14 2 3 4 5 6 7 8 9 AL

Blood, 51 hrs 11 58 3 119 1 0 1 1 612 0 0 70 6 5 39 926

Blood, 66 hrs 1 11 0 0 174 0 4 0 0 0 0 8 0 5 1 204

Blood, 73 hrs 6 290 3 9 3 0 2 0 7 0 0 4 1 32 6 363 109 Blood, 89 hrs 8 767 13 14 0 0 3 0 12 0 1 18 4 221 31 2

Blood, 112 hrs 6 51 94 0 1 0 8 0 1 0 0 25 0 25 13 224

Blood, 137 hrs 10 20 120 1 1 0 2 0 0 0 0 14 0 64 7 239 Blood, patient followup 139 2 11 46 0 1 0 0 0 0 0 70 60 2 85 416

Blood, control 00 29 2 2 3 1 0 0 0 0 0 0 9 1 1 12 60

Blood, control 01 517 0 38 74 0 11 0 0 0 0 0 65 119 2 28 854 159 Blood, control 02 1169 0 32 31 2 84 0 0 0 0 0 50 167 5 56 6

Blood, control 03 230 0 8 10 2 11 0 0 0 0 0 53 27 0 28 369

Hematoma, 51 hrs 0 0 0 0 0 0 0 0 0 418 7 2 0 0 0 427

Hematoma, 66 hrs 0 0 0 0 0 0 0 0 0 11 1 0 0 0 0 12

Hematoma, 73 hrs 1 3 0 8 0 0 3 734 9 57 24 56 0 4 11 910

Hematoma, 89 hrs 0 2 0 0 0 0 1 47 2 6 60 8 0 5 2 133 Hematoma, 112 hrs 0 0 0 2 0 0 5 14 1 6 207 17 0 0 13 265 Hematoma, 137 hrs 0 0 0 0 0 0 2 0 0 2 193 4 0 0 1 202

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Table S5. Monocyte sub-cluster IPA results. Top 10 pathways and associated genes are shown.

myeloid sub- Ingenuity Canonical Pathways -log(p-value) Ratio Molecules cluster

Retinoate Biosynthesis II 2.68 0.25 RBP7 Pentose Phosphate Pathway (Non-oxidative Branch) 2.5 0.167 TKT Clathrin-mediated Endocytosis Signaling 2.35 0.0104 AP1S2,LYZ Pentose Phosphate Pathway 2.28 0.1 TKT myeloid subcluster Role of IL-17A in Psoriasis 2.16 0.0769 S100A9 0 The Visual Cycle 1.98 0.05 RBP7 Retinoate Biosynthesis I 1.75 0.0294 RBP7 Retinol Biosynthesis 1.66 0.0238 RBP7 PFKFB4 Signaling Pathway 1.62 0.0217 TKT Autophagy 1.5 0.0164 CTSS LXR/RXR Activation 5.2 0.0331 CD14,CLU,IL1B,PTGS2 IL-10 Signaling 4.34 0.0435 CCR1,CD14,IL1B MIF-mediated Glucocorticoid Regulation 3.27 0.0588 CD14,PTGS2 MIF Regulation of Innate Immunity 3.08 0.0476 CD14,PTGS2 Role of myeloid subcluster Hypercytokinemia/hyperchemokinemia in the 1 Pathogenesis of Influenza 3.06 0.0465 CCR1,IL1B Eicosanoid Signaling 2.69 0.0303 LTA4H,PTGS2 Toll-like Receptor Signaling 2.57 0.0263 CD14,IL1B Sirtuin Signaling Pathway 2.52 0.0103 DUSP6,SOD2,TUBA1A Neuroinflammation Signaling Pathway 2.48 0.01 IL1B,PTGS2,SOD2 Glucocorticoid Receptor Signaling 2.34 0.00893 IL1B,PTGS2,SGK1 C5AR1,CCL2,CCL7,CXCL16,CXCL2,CXCL3,EZR,FPR1,IL1R1,IL1R2, Granulocyte Adhesion and Diapedesis 11.5 0.0833 IL1RAP,IL1RN,ITGA5,ITGB1,TNFRSF1A ACTB,ACTG1,C5AR1,CCL2,CCL7,CXCL16,CXCL2,CXCL3,EZR,IL1R myeloid subcluster Agranulocyte Adhesion and Diapedesis 9.93 0.0725 1,IL1RN,ITGA5,ITGB1,TNFRSF1A 2 CEBPB,CTNNB1,HES1,HIF1A,IL1R1,IL1R2,IL1RAP,ITGA5,ITGB1,NA Osteoarthritis Pathway 7.4 0.0569 MPT,S100A8,TNFRSF1A CCL2,CEBPB,CTNNB1,FTH1,HIF1A,IL1R1,IL1R2,IL1RAP,IL1RN,ITG Hepatic Fibrosis Signaling Pathway 6.37 0.038 A5,ITGB1,TFRC,TIMP1,TNFRSF1A

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Caveolar-mediated Endocytosis Signaling 6.04 0.0959 ACTB,ACTG1,CD55,ITGA5,ITGAX,ITGB1,PTPN1 TREM1 Signaling 5.96 0.0933 CCL2,CCL7,CXCL2,ITGA5,ITGAX,ITGB1,TREM1 LXR/RXR Activation 5.59 0.0661 CCL2,CCL7,IL1R1,IL1R2,IL1RAP,IL1RN,S100A8,TNFRSF1A Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes 4.23 0.0638 ACTB,ACTG1,EZR,FCGR1A,HMOX1,LCP2 Role of Macrophages, Fibroblasts and C5AR1,CCL2,CEBPB,CTNNB1,FCGR1A,IL1R1,IL1R2,IL1RAP,IL1RN Endothelial Cells in Rheumatoid Arthritis 4.07 0.0321 ,TNFRSF1A Leukocyte Extravasation Signaling 4.06 0.0406 ACTB,ACTG1,CD44,CTNNB1,EZR,ITGA5,ITGB1,TIMP1 Granzyme A Signaling 6.06 0.158 H1-2,H1-3,H1-4 Glucocorticoid Receptor Signaling 3.48 0.0119 ANXA1,CCNH,HSPA5,TSC22D3 Toll-like Receptor Signaling 2.57 0.0263 TNFAIP3,UBC Sirtuin Signaling Pathway 2.52 0.0103 H1-2,H1-3,H1-4

myeloid subcluster NER Pathway 2.32 0.0194 CCNH,H4C3 3 Trans, trans-farnesyl Diphosphate Biosynthesis 2.3 0.2 IDI1 Protein Kinase A Signaling 2.14 0.00754 H1-2,H1-3,H1-4 Necroptosis Signaling Pathway 1.96 0.0127 PELI1,UBC NF-κB Signaling 1.85 0.0112 PELI1,TNFAIP3 Mevalonate Pathway I 1.85 0.0714 IDI1 Glycolysis I 13 0.346 ALDOA,ENO1,ENO2,GAPDH,GPI,PFKP,PGK1,PKM,TPI1 DNAJA1,DNAJB1,HSP90AA1,HSP90AB1,HSP90B1,HSPA1A/HSPA1 Aldosterone Signaling in Epithelial Cells 11.1 0.0886 B,HSPA6,HSPA8,HSPB1,HSPD1,HSPE1,HSPH1,PDIA3,PIK3CB DNAJA1,DNAJB1,HSP90AA1,HSP90AB1,HSP90B1,HSPA1A/HSPA1 Protein Ubiquitination Pathway 7.97 0.0513 B,HSPA6,HSPA8,HSPB1,HSPD1,HSPE1,HSPH1,PSMA6,UBB Role of IL-17A in Psoriasis 7.67 0.385 CCL20,CXCL1,CXCL5,CXCL6,CXCL8

myeloid subcluster Gluconeogenesis I 7.61 0.231 ALDOA,ENO1,ENO2,GAPDH,GPI,PGK1 4 EGLN3,HSP90AA1,JUN,LDHA,MMP19,PIK3CB,RALA,SLC2A1,VEGF HIF1α Signaling 6.92 0.0796 A Role of IL-17A in Arthritis 6.9 0.127 CCL20,CXCL1,CXCL5,CXCL6,CXCL8,NFKBIA,PIK3CB Unfolded protein response 6.84 0.125 CD82,HSP90B1,HSPA1A/HSPA1B,HSPA6,HSPA8,HSPH1,PDIA6 Role of PKR in Interferon Induction and HSP90AA1,HSP90AB1,HSP90B1,HSPA1A/HSPA1B,HSPA6,HSPA8, Antiviral Response 6.79 0.0769 JUN,MARCO,NFKBIA CCL20,CCL3,CCL4,CXCL1,CXCL5,CXCL6,CXCL8,MMP19,PPBP,SD Granulocyte Adhesion and Diapedesis 6.16 0.0556 C2 FOXO3,HIPK2,MAP2K3,MAPKAPK2,PIK3R5,SERPINE1,SMAD7,TB myeloid subcluster Senescence Pathway 3.96 0.0291 K1 5 Phagosome Maturation 3.82 0.0397 ATP6V0B,CALR,CTSB,CTSV,RAB5C,TUBA1C

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FGF Signaling 2.99 0.0476 ATF4,MAP2K3,MAPKAPK2,PIK3R5 Death Receptor Signaling 2.86 0.044 LMNA,TBK1,TIPARP,TNFRSF21 PFKFB4 Signaling Pathway 2.73 0.0652 ATF4,FBP1,MAP2K3 Type I Diabetes Mellitus Signaling 2.54 0.036 CD86,IFNGR2,IRAK1,MAP2K3 Role of IL-17A in Arthritis 2.51 0.0545 MAP2K3,MAPKAPK2,PIK3R5 p38 MAPK Signaling 2.45 0.0339 ATF4,IRAK1,MAP2K3,MAPKAPK2 Clathrin-mediated Endocytosis Signaling 2.44 0.0259 APOC1,APOE,DAB2,PIK3R5,RAB5C Autophagy 2.38 0.0492 CTSB,CTSV,RB1CC1 RPL10A,RPL13A,RPL17,RPL22,RPL23A,RPL27A,RPL3,RPL35,RPL5 ,RPLP0,RPS11,RPS2,RPS21,RPS23,RPS24,RPS29,RPS3A,RPS4X, EIF2 Signaling 29.8 0.103 RPS5,RPS6,RPS7,RPS8,RPSA CD74,CIITA,HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA- DPB1,HLA-DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA- Antigen Presentation Pathway 26.3 0.359 DRB1,HLA-DRB5 HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- Allograft Rejection Signaling 17 0.14 DRB5 HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- OX40 Signaling Pathway 16.8 0.133 DRB5 HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- myeloid subcluster DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- 6 PD-1, PD-L1 cancer immunotherapy pathway 15.9 0.113 DRB5 RPS11,RPS2,RPS21,RPS23,RPS24,RPS29,RPS3A,RPS4X,RPS5,R Regulation of eIF4 and p70S6K Signaling 15.4 0.0828 PS6,RPS7,RPS8,RPSA HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- Th1 Pathway 15.2 0.0992 DRB5 HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- Th2 Pathway 14.6 0.0882 DRB5 RPS11,RPS2,RPS21,RPS23,RPS24,RPS29,RPS3A,RPS4X,RPS5,R mTOR Signaling 13.8 0.0619 PS6,RPS7,RPS8,RPSA HLA-DMA,HLA-DMB,HLA-DOA,HLA-DPA1,HLA-DPB1,HLA- DQA1,HLA-DQA2,HLA-DQB1,HLA-DQB2,HLA-DRA,HLA-DRB1,HLA- Cdc42 Signaling 13.5 0.0719 DRB5 RPL13,RPL14,RPL27,RPL30,RPL31,RPL34,RPL35A,RPL38,RPL39, RPL4,RPL41,RPS10,RPS12,RPS15A,RPS20,RPS25,RPS27,RPS27A EIF2 Signaling 32.6 0.0893 ,RPS3,RPS4Y1 myeloid subcluster RPS10,RPS12,RPS15A,RPS20,RPS25,RPS27,RPS27A,RPS3,RPS4 7 Regulation of eIF4 and p70S6K Signaling 12.3 0.0573 Y1 RPS10,RPS12,RPS15A,RPS20,RPS25,RPS27,RPS27A,RPS3,RPS4 mTOR Signaling 11.2 0.0429 Y1 CTLA4 Signaling in Cytotoxic T Lymphocytes 3.62 0.0337 CD3D,CD3G,TRAT1

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iCOS-iCOSL Signaling in T Helper Cells 3.34 0.027 CD3D,CD3G,TRAT1 Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 3.01 0.0588 CD3D,CD3G Antiproliferative Role of TOB in T Cell Signaling 2.89 0.0513 CD3D,CD3G Hematopoiesis from Pluripotent Stem Cells 2.69 0.0408 CD3D,CD3G Primary Immunodeficiency Signaling 2.67 0.04 CD3D,IL7R Nur77 Signaling in T Lymphocytes 2.53 0.0339 CD3D,CD3G EIF2 Signaling 2.48 0.00893 RPLP2,RPS28 Pathogenesis of Multiple Sclerosis 2.45 0.111 CCL5 Differential Regulation of Cytokine Production in Macrophages and T Helper Cells by IL-17A and IL-17F 2.15 0.0556 CCL5 Differential Regulation of Cytokine Production in Intestinal Epithelial Cells by IL-17A and IL- 17F 2.04 0.0435 CCL5 myeloid subcluster IL-17A Signaling in Gastric Cells 2.01 0.04 CCL5 8 Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza 1.77 0.0233 CCL5 Role of IL-17A in Arthritis 1.67 0.0182 CCL5 Role of PI3K/AKT Signaling in the Pathogenesis of Influenza 1.6 0.0156 CCL5 VDR/RXR Activation 1.52 0.0128 CCL5 Role of MAPK Signaling in the Pathogenesis of Influenza 1.5 0.0125 CCL5 Tec Kinase Signaling 3.91 0.0305 GNG2,HCK,ITGA4,LYN,RHOC B Cell Receptor Signaling 3.66 0.027 LYN,PAG1,PIK3AP1,POU2F2,PTPN6 Reelin Signaling in Neurons 3.24 0.031 HCK,ITGA4,ITGAL,LYN Systemic Lupus Erythematosus In B Cell Signaling Pathway 2.88 0.0182 HCK,LYN,PAG1,PIK3AP1,PTPN6

myeloid subcluster Agrin Interactions at Neuromuscular Junction 2.78 0.038 ITGA4,ITGAL,UTRN 9 Polyamine Regulation in Colon Cancer 2.72 0.0909 PSME2,SAT1 Tumoricidal Function of Hepatic Natural Killer Cells 2.64 0.0833 BID,ITGAL Production of Nitric Oxide and Reactive Oxygen Species in Macrophages 2.63 0.0213 PTPN6,RHOC,SERPINA1,TNFRSF1B Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes 2.57 0.0319 FCGR3A/FCGR3B,HCK,LYN Natural Killer Cell Signaling 2.56 0.0203 FCGR3A/FCGR3B,ITGAL,LILRB1,PTPN6

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Interferon Signaling 12.8 0.194 IFI6,IFIT1,IFIT3,ISG15,MX1,OAS1,STAT1 Systemic Lupus Erythematosus In B Cell Signaling Pathway 6.49 0.0255 FOS,IFIT2,IFIT3,ISG15,STAT1,TNFSF10,TNFSF13B Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses 5.25 0.0325 EIF2AK2,OAS1,OAS2,TNFSF10,TNFSF13B UVA-Induced MAPK Signaling 4.67 0.0408 FOS,PARP14,PARP9,STAT1 Role of PKR in Interferon Induction and myeloid subcluster Antiviral Response 4.37 0.0342 EIF2AK2,FOS,HSPA4,STAT1 10 Inhibition of ARE-Mediated mRNA Degradation Pathway 4.29 0.0328 TNFSF10,TNFSF13B,ZFP36,ZFP36L2 Retinoic acid Mediated Apoptosis Signaling 3.86 0.05 PARP14,PARP9,TNFSF10 Activation of IRF by Cytosolic Pattern Recognition Receptors 3.8 0.0476 IFIT2,ISG15,STAT1 PDGF Signaling 3.4 0.0349 EIF2AK2,FOS,STAT1 Death Receptor Signaling 3.32 0.033 PARP14,PARP9,TNFSF10 Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 5.27 0.0882 CD247,GZMB,PRF1 Th1 and Th2 Activation Pathway 4.61 0.0234 CD247,IL2RB,JUN,KLRD1 Natural Killer Cell Signaling 4.37 0.0203 CD247,IL2RB,KLRB1,KLRD1 Crosstalk between Dendritic Cells and Natural Killer Cells 4.01 0.0337 IL2RB,KLRD1,PRF1 myeloid subcluster Granzyme B Signaling 3.93 0.125 GZMB,PRF1 11 Granzyme A Signaling 3.78 0.105 GZMA,PRF1 Type I Diabetes Mellitus Signaling 3.73 0.027 CD247,GZMB,PRF1 Tumoricidal Function of Hepatic Natural Killer Cells 3.57 0.0833 GZMB,PRF1 Th2 Pathway 3.47 0.0221 CD247,IL2RB,JUN Graft-versus-Host Disease Signaling 2.97 0.0417 GZMB,PRF1 Primary Immunodeficiency Signaling 4.65 0.06 IGHA1,LCK,ZAP70 Iron homeostasis signaling pathway 3.35 0.0219 CIAO2B,HBA1/HBA2,HBB Calcium-induced T Lymphocyte Apoptosis 2.62 0.0303 LCK,ZAP70 Systemic Lupus Erythematosus In B Cell myeloid subcluster Signaling Pathway 2.48 0.0109 IGHA1,ISG20,LCK 12 CTLA4 Signaling in Cytotoxic T Lymphocytes 2.37 0.0225 LCK,ZAP70 T Cell Receptor Signaling 2.23 0.019 LCK,ZAP70 PD-1, PD-L1 cancer immunotherapy pathway 2.22 0.0189 LCK,ZAP70 iCOS-iCOSL Signaling in T Helper Cells 2.18 0.018 LCK,ZAP70

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CD28 Signaling in T Helper Cells 2.12 0.0167 LCK,ZAP70 IL-15 Production 2.11 0.0165 LCK,ZAP70 Remodeling of Epithelial Adherens Junctions 3.24 0.0294 TUBB1,VCL Epithelial Adherens Junction Signaling 2.55 0.0132 TUBB1,VCL Gαq Signaling 2.52 0.0127 GNG11,RGS18 Germ Cell-Sertoli Cell Junction Signaling 2.45 0.0117 TUBB1,VCL myeloid subcluster Sertoli Cell-Sertoli Cell Junction Signaling 2.38 0.0108 TUBB1,VCL 13 Opioid Signaling Pathway 2.14 0.0081 GNG11,RGS18 Extrinsic Prothrombin Activation Pathway 2.07 0.0625 F13A1 G Protein Signaling Mediated by Tubby 1.79 0.0323 GNG11 Coagulation System 1.74 0.0286 F13A1 Intrinsic Prothrombin Activation Pathway 1.66 0.0238 F13A1 Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 6.89 0.118 B2M,HLA-B,HLA-C,HLA-E Antigen Presentation Pathway 6.65 0.103 B2M,HLA-B,HLA-C,HLA-E Natural Killer Cell Signaling 6.61 0.0305 B2M,FCGR3A/FCGR3B,HLA-B,HLA-C,HLA-E,LIMK2 Caveolar-mediated Endocytosis Signaling 5.54 0.0548 B2M,HLA-B,HLA-C,HLA-E myeloid subcluster Cdc42 Signaling 5.53 0.0299 B2M,HLA-B,HLA-C,HLA-E,LIMK2 14 Granulocyte Adhesion and Diapedesis 5.37 0.0278 CSF3R,CXCR2,FPR2,MMP25,SELL Dendritic Cell Maturation 5.34 0.0273 B2M,FCGR3A/FCGR3B,HLA-B,HLA-C,HLA-E Allograft Rejection Signaling 5.25 0.0465 B2M,HLA-B,HLA-C,HLA-E CTLA4 Signaling in Cytotoxic T Lymphocytes 5.19 0.0449 B2M,HLA-B,HLA-C,HLA-E OX40 Signaling Pathway 5.17 0.0444 B2M,HLA-B,HLA-C,HLA-E

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Table S6. Human monocyte gene sets used for module scoring. Gene Set Name Description Reference CD141_CLEC9A Generated from unstimulated human Villani et al.3 _Villani PBMCs, identified by scRNA-seq. CD1C_A_Villani Generated from unstimulated human Villani et al.3 PBMCs, identified by scRNA-seq. CD1C_B_Villani Generated from unstimulated human Villani et al.3 PBMCs, identified by scRNA-seq. CD1Cminus_CD Generated from unstimulated human Villani et al.3 141minus_Villani PBMCs, identified by scRNA-seq. New_pop_Villani Generated from unstimulated human Villani et al.3 PBMCs, identified by scRNA-seq. pDC_Villani Generated from unstimulated human Villani et al.3 PBMCs, identified by scRNA-seq. XUE_Module1 to Generated by comparative/ integrative Xue et al.3 XUE_Module49 analysis on several microarray datasets. Monocyte_huma ImmGenn database Shay et al.2 n MyeloidDC_hum ImmGenn database Shay et al.2 an PlasmacytoidDC ImmGenn database Shay et al.2 _human

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Table S7. Wilcox rank sum pairwise test results for CD1CB (CD14+ monocyte) signature module scores, data

presented in Figure 2. Table displays adjusted p values (padj) for pairwise comparisons for each indicated myeloid sub-

cluster. P values are colored as follows: blue (0.01 ≤ padj < 0.05, *), purple (0.001 ≤ padj < 0.01, **), white (0 ≤ padj < 0.001, ***), gray (not significant, padj < 0.05).

Sub- 0 1 2 3 4 5 6 7 8 9 10 11 12 13 cluster 1 1.68E-47 ------2 6.19E-71 3.28E-10 ------3 1.15E-16 1.72E-57 1.66E-69 ------3.08E- 3.56E- 1.74E- 1.74E- 4 ------249 225 193 152 2.06E- 2.56E- 3.16E- 5.98E- 5 2.91E-08 ------224 212 183 125 2.95E- 7.73E- 1.50E- 6 6.25E-98 2.16E-06 6.31E-01 ------187 189 166 8.67E- 2.51E- 4.04E- 2.00E- 7 1.03E-65 1.06E-14 7.82E-78 ------103 100 106 54 6.62E- 4.97E- 3.87E- 8 6.51E-92 2.32E-30 3.11E-92 1.19E-60 4.82E-06 ------120 112 38 4.48E- 3.41E- 1.08E- 9.39E- 1.00E- 1.35E- 1.01E- 9 3.03E-38 1.19E-52 - - - - - 184 169 151 134 37 104 98 1.66E- 1.91E- 4.21E- 3.08E- 3.85E- 10 1.29E-02 6.82E-24 1.02E-35 5.30E-04 6.68E-26 - - - - 117 103 86 41 104 3.53E- 3.85E- 1.29E- 11 1.80E-39 2.42E-71 4.43E-75 5.83E-09 1.75E-72 6.97E-55 3.48E-01 1.62E-78 - - - 41 06 16 9.99E- 1.16E- 9.52E- 1.63E- 12 2.78E-02 1.59E-17 4.05E-26 3.21E-03 1.69E-83 1.16E-74 6.67E-21 1.11E-76 - - 63 32 01 13 6.63E- 9.59E- 1.14E- 6.67E- 1.23E- 13 7.50E-04 4.59E-15 1.94E-20 4.75E-01 2.89E-52 1.81E-45 5.65E-09 1.69E-51 - 37 17 01 06 01 1.04E- 3.47E- 4.53E- 3.72E- 3.39E- 3.69E- 14 4.48E-08 1.68E-12 2.35E-13 1.57E-03 1.35E-14 1.28E-10 4.92E-01 1.00E-17 07 01 06 01 06 04

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Table S8. T cell sub-clustering analysis cluster membership cell counts. Sub-clusters are indicated by NT0-NT11.

Condition (Compartment, Time) NT 0 NT 1 NT 2 NT 3 NT 4 NT 5 NT 6 NT 7 NT 8 NT 9 NT 10 NT 11 Total Blood, 51 hrs 34 1 10 323 268 17 0 1 0 52 15 0 721 Blood, 66 hrs 1 0 17 0 3 0 3 4 0 0 0 0 28 Blood, 73 hrs 27 0 485 21 118 0 0 14 0 22 8 0 695 Blood, 89 hrs 3 0 49 1 101 0 1 8 0 10 11 0 184 Blood, 112 hrs 326 0 579 8 237 0 3 456 0 55 12 5 1681 Blood, 137 hrs 4 0 365 0 14 0 6 22 1 2 0 53 467 Blood, patient followup 645 0 6 6 95 0 0 0 0 58 48 0 858 Blood, control 00 1069 1 1 16 109 0 0 2 0 45 17 0 1260 Blood, control 01 424 0 4 1 61 0 0 0 0 18 15 0 523 Blood, control 02 133 0 2 3 5 0 0 0 0 3 3 0 149 Blood, control 03 555 1 11 6 325 0 0 4 0 29 8 0 939 Hematoma, 51 hrs 4 4 2 18 8 1087 1 11 0 0 0 0 1135 Hematoma, 66 hrs 3 7 3 1 0 217 4 11 2 0 0 0 248 Hematoma, 73 hrs 194 37 345 1266 190 19 81 56 0 133 11 0 2332 Hematoma, 89 hrs 2 116 54 66 23 4 1014 122 4 15 4 0 1424 Hematoma, 112 hrs 25 1774 6 45 10 1 43 355 18 54 12 5 2348 Hematoma, 137 hrs 2 1064 2 0 0 5 12 98 692 9 2 5 1891

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Table S9. T cell sub-cluster IPA results. Top 10 pathways and associated genes are shown.

T cell sub-cluster Ingenuity Canonical Pathways -log(p-value) Ratio Molecules

EIF2 Signaling 3.16 0.0134 PABPC1,RPL9,RPS3A Regulation of eIF4 and p70S6K Signaling 2.17 0.0127 PABPC1,RPS3A Antiproliferative Role of TOB in T Cell Signaling 1.52 0.0256 PABPC1 Hepatic Fibrosis Signaling Pathway 1.47 0.00543 FTH1,PDCD4 Transcriptional Regulatory Network in Embryonic Stem Cells 1.38 0.0185 SKIL T cell subcluster 1 SAPK/JNK Signaling 1.11 0.0098 DUSP4 PD-1, PD-L1 cancer immunotherapy pathway 1.09 0.00943 PDCD4 Inhibition of ARE-Mediated mRNA Degradation Pathway 1.03 0.0082 CNOT1 Iron homeostasis signaling pathway 0.987 0.0073 FTH1 Granulocyte Adhesion and Diapedesis 0.873 0.00556 PPBP Role of IL-17A in Psoriasis 5.2 0.154 S100A8,S100A9 LXR/RXR Activation 3.24 0.0165 LYZ,S100A8 Atherosclerosis Signaling 3.2 0.0159 LYZ,S100A8 IL-12 Signaling and Production in Macrophages 3.16 0.0152 LYZ,S100A8 Production of Nitric Oxide and Reactive T cell subcluster 2 Oxygen Species in Macrophages 2.86 0.0106 LYZ,S100A8 Clathrin-mediated Endocytosis Signaling 2.83 0.0104 LYZ,S100A8 Osteoarthritis Pathway 2.76 0.00948 S100A8,S100A9 Autophagy 1.73 0.0164 CTSS Adipogenesis pathway 1.39 0.00746 TXNIP Phagosome Maturation 1.34 0.00662 CTSS GADD45 Signaling 3.86 0.105 CDKN1A,GADD45A HGF Signaling 3.85 0.027 CDKN1A,FOS,ITGB1 Systemic Lupus Erythematosus In T Cell T cell subcluster 3 Signaling Pathway 3.65 0.012 CREM,FOS,GADD45A,ICOS TNFR2 Signaling 3.46 0.0667 FOS,TNFAIP3 Cell Cycle: G2/M DNA Damage Checkpoint Regulation 3.03 0.0408 CDKN1A,GADD45A

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TNFR1 Signaling 3.01 0.04 FOS,TNFAIP3 CD40 Signaling 2.79 0.0308 FOS,TNFAIP3 Toll-like Receptor Signaling 2.65 0.0263 FOS,TNFAIP3 VDR/RXR Activation 2.63 0.0256 CDKN1A,GADD45A Role of BRCA1 in DNA Damage Response 2.61 0.025 CDKN1A,GADD45A IL-17A Signaling in Gastric Cells 5.28 0.12 CCL5,FOS,JUN Chemokine Signaling 3.75 0.0375 CCL5,FOS,JUN Granzyme B Signaling 3.67 0.125 GZMB,PRF1 CCR5 Signaling in Macrophages 3.55 0.0319 CCL5,FOS,JUN Granzyme A Signaling 3.52 0.105 GZMA,PRF1 T cell subcluster 4 Tumoricidal Function of Hepatic Natural Killer Cells 3.31 0.0833 GZMB,PRF1 Neuroprotective Role of THOP1 in Alzheimer's Disease 3.27 0.0256 GZMA,GZMB,GZMK Renin-Angiotensin Signaling 3.26 0.0254 CCL5,FOS,JUN TNFR2 Signaling 3.12 0.0667 FOS,JUN Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 3.01 0.0588 GZMB,PRF1 DNAJA1,DNAJB1,HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA Aldosterone Signaling in Epithelial Cells 14.2 0.0759 5,HSPA6,HSPA8,HSPB1,HSPD1,HSPE1,HSPH1 DNAJA1,DNAJB1,HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA Protein Ubiquitination Pathway 14.2 0.0513 5,HSPA6,HSPA8,HSPB1,HSPD1,HSPE1,HSPH1,UBB,UBC Unfolded protein response 8.2 0.107 DDIT3,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8,HSPH1 ANXA1,CCL5,HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA5,HS Glucocorticoid Receptor Signaling 7.91 0.0298 PA6,HSPA8,JUN,TAF7 Role of PKR in Interferon Induction and HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8,J T cell subcluster 5 Antiviral Response 7.71 0.0598 UN BAG2 Signaling Pathway 7.09 0.116 HSP90AA1,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8 Huntington's Disease Signaling 6.82 0.0338 DNAJB1,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8,JUN,UBB,UBC eNOS Signaling 5.49 0.0377 HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8 NRF2-mediated Oxidative Stress Response 5.06 0.0317 DNAJA1,DNAJB1,FTL,JUN,SQSTM1,UBB Natural Killer Cell Signaling 3.83 0.0254 FYN,HSPA1A/HSPA1B,HSPA5,HSPA6,HSPA8 Role of IL-17A in Psoriasis 7.76 0.308 CXCL3,CXCL8,S100A8,S100A9 T cell subcluster 6 TREM1 Signaling 7.72 0.08 CCL2,CCL7,CD83,CXCL8,IL1B,TYROBP Hepatic Fibrosis Signaling Pathway 6.86 0.0245 CCL2,CXCL8,FOS,FTH1,HIF1A,IL1B,MYL6,SOD2,TIMP1

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Agranulocyte Adhesion and Diapedesis 6.56 0.0363 CCL2,CCL7,CXCL3,CXCL8,IL1B,MYL6,PPBP Role of IL-17F in Allergic Inflammatory Airway Diseases 5.59 0.0952 CCL2,CCL7,CXCL8,IL1B Granulocyte Adhesion and Diapedesis 5.46 0.0333 CCL2,CCL7,CXCL3,CXCL8,IL1B,PPBP Neuroinflammation Signaling Pathway 5.28 0.0233 CCL2,CXCL8,FOS,HLA-DRA,IL1B,SOD2,TYROBP Role of IL-17A in Arthritis 5.12 0.0727 CCL2,CCL7,CXCL3,CXCL8 LXR/RXR Activation 5.08 0.0413 CCL2,CCL7,IL1B,LYZ,S100A8 Osteoarthritis Pathway 5.07 0.0284 CXCL8,HIF1A,IL1B,NAMPT,S100A8,S100A9 Oxidative Phosphorylation 6.1 0.0642 MT-ATP6,MT-CO2,MT-CO3,MT-CYB,MT-ND2,MT-ND3,MT-ND5 CASP8,MT-ATP6,MT-CO2,MT-CO3,MT-CYB,MT-ND2,MT-ND3,MT- Mitochondrial Dysfunction 5.85 0.0468 ND5 DNA Double-Strand Break Repair by Homologous Recombination 2.72 0.143 ATM,ATRX DNA Methylation and Transcriptional Repression Signaling 1.94 0.0571 ARID4B,CHD4

T cell subcluster 7 Sirtuin Signaling Pathway 1.93 0.0172 MT-ATP6,MT-CYB,MT-ND2,MT-ND3,MT-ND5 Cell Cycle: G2/M DNA Damage Checkpoint Regulation 1.66 0.0408 ATM,MDM4 Transcriptional Regulatory Network in Embryonic Stem Cells 1.58 0.037 KAT6A,RIF1 PCP pathway 1.49 0.0333 JUND,ROCK1 HOTAIR Regulatory Pathway 1.41 0.0187 KMT2A,KMT2C,ROCK1 Ephrin B Signaling 1.35 0.0278 ITSN2,ROCK1 Role of IL-17A in Arthritis 3.36 0.0545 CCL5,CXCL8,MAPKAPK2 Autophagy 3.23 0.0492 CTSL,RB1CC1,SQSTM1 IL-17A Signaling in Gastric Cells 2.69 0.08 CCL5,CXCL8 p53 Signaling 2.63 0.0306 HIF1A,JMY,PMAIP1 Hepatic Fibrosis Signaling Pathway 2.51 0.0136 CCL5,CXCL8,HIF1A,PDCD4,SPP1 T cell subcluster 8 Adipogenesis pathway 2.25 0.0224 DDIT3,HIF1A,NR1D2 Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza 2.22 0.0465 CCL5,CXCL8 BAG2 Signaling Pathway 2.22 0.0465 HSPA4,MAPKAPK2 Unfolded protein response 2 0.0357 DDIT3,HSPA4 Granulocyte Adhesion and Diapedesis 1.9 0.0167 CCL5,CXCL8,PPBP ARPC1B,ARPC2,ARPC3,ARPC5,CD3D,CD3G,CFL1,HLA- T cell subcluster 9 Cdc42 Signaling 20.3 0.0958 DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA-DRA,HLA-

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DRB1,HLA-DRB5,ITGB1,MYL6 ACTB,ACTG1,ARPC1B,ARPC2,ARPC3,ARPC5,CFL1,FLNA,ITGB1, Actin Cytoskeleton Signaling 13.7 0.0596 MYL6,PFN1,TMSB10/TMSB4X,TTN CD74,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA- Antigen Presentation Pathway 13 0.205 DRA,HLA-DRB1,HLA-DRB5 ARPC1B,ARPC2,ARPC3,ARPC5,CD3D,CD3G,HLA-DQA1,HLA- CD28 Signaling in T Helper Cells 12.1 0.0833 DRA,HLA-DRB1,HLA-DRB5 ACTB,ACTG1,ARPC1B,ARPC2,ARPC3,ARPC5,CFL1,MYL6,PFN1, RhoA Signaling 12 0.0813 TTN CD3D,CD3G,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA- OX40 Signaling Pathway 11.6 0.1 DRA,HLA-DRB1,HLA-DRB5 CD3D,CD3G,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA- Th2 Pathway 11.5 0.0735 DRA,HLA-DRB1,HLA-DRB5,MAF Regulation of Actin-based Motility by Rho 11.5 0.0957 ACTB,ARPC1B,ARPC2,ARPC3,ARPC5,CFL1,ITGB1,MYL6,PFN1 CD3D,CD3G,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA- Th1 and Th2 Activation Pathway 10.5 0.0585 DRA,HLA-DRB1,HLA-DRB5,MAF CD3D,CD3G,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQA2,HLA- Th1 Pathway 10.5 0.0744 DRA,HLA-DRB1,HLA-DRB5 ATP5F1A,ATP5F1B,ATP5F1C,ATP5F1E,ATP5MC1,ATP5MC3,ATP 5MF,ATP5MG,ATP5PB,ATP5PF,ATP5PO,COX5A,COX6A1,COX6B 1,COX6C,COX7B,COX8A,NDUFA1,NDUFA11,NDUFA12,NDUFA13 ,NDUFA2,NDUFA4,NDUFA6,NDUFB1,NDUFB11,NDUFB2,NDUFB3 Oxidative Phosphorylation 31.8 0.303 ,NDUFB6,NDUFB7,NDUFB9,UQCR10,UQCRQ ATP5F1A,ATP5F1B,ATP5F1C,ATP5F1E,ATP5MC1,ATP5MC3,ATP 5MF,ATP5MG,ATP5PB,ATP5PF,ATP5PO,COX5A,COX6A1,COX6B 1,COX6C,COX7B,COX8A,NDUFA1,NDUFA11,NDUFA12,NDUFA13 ,NDUFA2,NDUFA4,NDUFA6,NDUFB1,NDUFB11,NDUFB2,NDUFB3 ,NDUFB6,NDUFB7,NDUFB9,PARK7,PRDX3,UCP2,UQCR10,UQCR Mitochondrial Dysfunction 31 0.222 Q,VDAC1,VDAC3 ATP5F1A,ATP5F1B,ATP5F1C,ATP5F1E,ATP5MC1,ATP5PB,ATP5 PF,H1-2,H1-3,H1-4,H1- 5,IDH2,LDHB,NDUFA1,NDUFA11,NDUFA12,NDUFA13,NDUFA2,N T cell subcluster 10 DUFA4,NDUFA6,NDUFB1,NDUFB11,NDUFB2,NDUFB3,NDUFB6,N DUFB7,NDUFB9,PARP1,PRKDC,RBBP8,SLC25A5,TUBA1B,UCP2, Sirtuin Signaling Pathway 21.2 0.127 VDAC1,VDAC3,XRCC5,XRCC6 Granzyme A Signaling 12.9 0.526 ANP32A,GZMA,H1-2,H1-3,H1-4,H1-5,HMGB2,NME1,PRF1,SET ACTB,ACTG1,ACTR2,ACTR3,ARPC1B,ARPC2,ARPC3,ARPC4,AR PC5,CDC42,CFL1,FLNA,IQGAP1,MSN,MYH9,MYL6,PFN1,RAC2,R Actin Cytoskeleton Signaling 10.7 0.101 HOA,TLN1,TMSB10/TMSB4X,WAS ACTB,ACTR2,ACTR3,ARPC1B,ARPC2,ARPC3,ARPC4,ARPC5,CD Regulation of Actin-based Motility by Rho 10.3 0.16 C42,CFL1,MYL6,PFN1,RAC2,RHOA,WAS ACTB,ACTG1,ACTR2,ACTR3,ARPC1B,ARPC2,ARPC3,ARPC4,AR Remodeling of Epithelial Adherens Junctions 10.1 0.191 PC5,IQGAP1,NME1,TUBA1B,TUBB CALR,CD74,HLA-DPA1,HLA-DPB1,HLA-DRA,HLA-DRB1,HLA- Antigen Presentation Pathway 9.21 0.256 DRB5,IFNG,PSMB8,PSMB9 ACTB,ACTG1,ACTR2,ACTR3,ARPC1B,ARPC2,ARPC3,ARPC4,AR Epithelial Adherens Junction Signaling 9.13 0.112 PC5,CDC42,IQGAP1,MYH9,MYL6,RHOA,TUBA1B,TUBB,WAS

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ACTB,ACTG1,ACTR2,ACTR3,ARPC1B,ARPC2,ARPC3,ARPC4,AR PC5,CDC42,CFL1,GNAI2,IQGAP1,MSN,MYL6,RAC2,RHOA,SEPTI Signaling by Rho Family GTPases 9 0.0861 N6,STMN1,VIM,WAS Role of IL-17A in Psoriasis 4.96 0.231 CXCL8,S100A8,S100A9 Polyamine Regulation in Colon Cancer 2.59 0.0909 MXD1,SAT1 Lipid Antigen Presentation by CD1 2.44 0.0769 FCER1G,PSAP Dendritic Cell Maturation 2.44 0.0219 FCER1G,HLA-DRA,PLCE1,TYROBP Communication between Innate and Adaptive Immune Cells 2.35 0.0312 CXCL8,FCER1G,HLA-DRA T cell subcluster 11 Osteoarthritis Pathway 2.22 0.019 CXCL8,FGFR1,S100A8,S100A9 Cardiac Hypertrophy Signaling (Enhanced) 2.18 0.0123 CACNA1A,CXCL8,CYBB,FGFR1,GNAQ,PLCE1 GPCR-Mediated Nutrient Sensing in Enteroendocrine Cells 2.17 0.0268 CACNA1A,GNAQ,PLCE1 LXR/RXR Activation 2.08 0.0248 HMGCR,LYZ,S100A8 Phagosome Formation 2.04 0.024 CLEC7A,FCER1G,PLCE1

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Table S10. Human monocyte gene sets used for module scoring. Gene Set Name Description Reference Szabo gene sets Generated from resting and stimulated Szabo et al.6 human T cells isolated from blood and tissues (lungs, lymph nodes, bone marrow). Miao gene sets Generated from summary of marker genes Miao et al.7 across the literature (Table S1). Cano-Gamez Generated from unstimulated and Cano-Gamez gene sets stimulated human T cells, identified by et al.8 scRNA-seq.

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