Gating strategy and analysis of various cell populations.

Each patient sample tube contained cryopreserved PBMCs that were derived from 2 mL whole blood. For control PBMC samples, 2 × 10⁶ cells were used per tube. The process of preparing PBMCs removes RBCs, neutrophils, and platelets from the sample, so very few of these events are noted, but they were still gated out for downstream analysis. A standard gating strategy (Figure 1) was used to define B cells (CD19⁺), T cells (CD3⁺), NK cells (CD3^–CD19^–CD7⁺CD56⁺), NKT cells (CD3⁺CD161⁺CD56⁺CD16⁺), basophils (CD3^–CD19^–CD123⁺HLA-DR^–), monocytes (Lin^–CD14⁺ and/or CD16⁺), and DCs (Lin^–CD14^–CD16^–HLA-DR⁺). B cells were further classified as plasmablasts (CD38⁺CD27⁺), naive (CD27^–), or memory (CD27⁺), and subgrouped as class-switched (IgM^–) or non-class switched (IgM⁺). T cells were classified as CD4⁺ or CD8⁺, and activated cells were defined as Ki-67⁺CD38⁺. NK cells were classified as CD16⁺ or CD16^–. Monocytes were classified as classical (CD14⁺CD16^–), intermediate (CD14⁺CD16⁺), or nonclassical (CD14^–CD16⁺). DCs were further defined as being plasmacytoid (pDC, CD123⁺) or myeloid (mDC, CD11c⁺). From this gating strategy, the frequency of each cell type in each sample was determined as a function of non-neutrophil CD45⁺ events (Figure 2A). Patient samples are shown in comparison to the range obtained from the control samples (gray shading). CD4⁺ T cells were activated in all patients, as previously published. One striking feature of CD8⁺ T cell activation was the decreased frequency of activated CD8⁺ T cells in EVD9 relative to the other patients. This has relevance as a predictor of clinical outcome, as other groups have shown an association between T cell function and survival (3). Indeed, patient EVD9 was seriously ill and would not have survived without intensive care support, while the other 3 patients in the cohort had more moderate disease (40, 42). Importantly the decrease in activated CD8⁺ T cells in EVD9 was only appreciated because here, activated CD8⁺ T cells were enumerated as a function of non-neutrophil CD45⁺ events rather than as a proportion of total T cells, as done previously. In the prior analysis, the magnitude of CD8⁺ T cell activation as a function of total T cells was similar across all donors (23), and we found a comparable activation of CD8⁺ T cells as a function of total T cells by CyTOF in present study (data not shown). Using an enumeration based on CD45⁺ events also revealed a previously unappreciated cell population. This occurs because the cell population that makes up the extra CD45⁺ events in EVD9 remains largely unaccounted for in traditional gating since it lacks lineage markers, but in our analysis was found to be CD11b⁺, Ki-67⁺, and pSTAT3⁺ (Figure 2B). In EVD9, this population ranged from 1% to as much as 17% (on day 14 after symptom onset) of all of the gated CD45⁺ events. The other 3 EVD patients also had this cell population, albeit at much lower levels than EVD9 (data not shown). Importantly, control patients did not have substantial quantities of this cell population, and EVD9 had the most. This suggests that the population could have relevance to disease, since EVD9 was severely ill, required extracorporeal support, and would not have survived without intensive critical care. All 4 patients also received experimental therapeutics, some of which could have altered their immune responses (23).

Open in a separate window

Figure 1

Gating strategy.

The gating strategy is represented using the day 25 sample from patient EVD5. Common PBMC populations are identified. cMonocytes, classical monocytes; intMonocytes, intermediate monocytes; ncMonocytes; nonclassical monocytes.

Open in a separate window

Figure 2

Frequencies and activation status of various cell populations.

(A) Frequencies of T cells, B cells, basophils, and NK cells as a percentage of CD45⁺CD66^– leukocytes in patients compared with normal healthy controls (range for all controls in gray). Fold increase in median Ki-67 signal as compared with that in a control population is shown for NK and NKT cells at each patient time point. D2, day 2. (B) The expanded Lin^–CD14^–CD16^–HLA-DR^– but CD11b⁺ population that was noted in EVD9 is shown, with histograms demonstrating pSTAT3 and Ki-67 expression in these cells.

The frequency of basophils was elevated in several of the patients at different time points as compared with controls. NKT cells were largely in the control range, and NK cells were low in 1 patient but normal or elevated in the others. Increased proliferation, as evidenced by Ki-67 staining in both NKT and NK cells, was noted in acute-phase samples from all patients (Figure 2A).

As previously reported, plasmablasts were increased in all 4 patients, most prominently in EVD5 at 17 days after symptom onset. Notably, EVD5 was also shown to have the highest numbers of EBOV GP–specific memory B cells in a detailed analysis of the B cell responses of these 4 patients (30). In all patients, samples from all time points contained more class-switched plasmablasts than IgM⁺ plasmablasts. Frequencies of naive, class-switched memory, and non-class-switched memory B cells were similar to those of controls.

Overall frequencies of classical monocytes were elevated in 3 of the 4 patients at the times sampled, while intermediate and nonclassical monocytes were in the normal range when considered as a frequency of CD45⁺ events. Using frequencies of non-neutrophil CD45⁺ events underestimates the dramatic shifts that occur in these populations during the course of the infection (Figure 3A, graphs). Previous work by others had demonstrated decreased numbers of CD16⁺ monocytes in association with fatal EVD (2), so we evaluated monocytes over time in this cohort of patients. In all patients, an almost complete loss of nonclassical and intermediate monocytes was noted at the earliest time point evaluated (Figure 3A, flow plots), followed by repopulation of first intermediate and then nonclassical monocytes. Notably, this was accompanied by a marked increase in proliferation of classical monocytes that preceded the appearance of intermediate monocytes (Figure 3B).

Open in a separate window

Figure 3

Monocyte populations are lost and then recover during EVD.

(A) Loss of intermediate and nonclassical monocytes during the acute viremic phase followed by recovery, of first intermediate and then nonclassical monocytes, in convalescence. Quantitation of these cell populations as a percentage of CD45⁺CD66^– leukocytes is shown in graphs (range for all controls in gray). (B) CD14⁺ monocytes are actively proliferating (Ki-67⁺) during the acute phase at time points in which viral loads are declining. Samples from a control obtained on 3 separate dates are shown for comparison.

mDC levels were lower than normal at the first time point evaluated in all 4 patients (Figure 4A, graphs). The dramatic loss of mDCs that occurred during the acute phase was more readily appreciated by visualizing raw flow plots (Figure 4A) and was most prominent in the first sample from EVD9, which represents a time in the infection when viral load control had not yet been achieved. Following viral load control and into convalescence, the mDC population returned to control levels in all patients. Subsets of mDCs were also evaluated, and CD1c⁺ mDCs showed the most striking decline during the acute phase, with robust return and increased expression of CD1c on mDCs during convalescence (Figure 4B). CD141⁺ mDCs were also evaluated, but an insufficient number were identified in all patients at all time points to make any conclusions regarding this subset.

Open in a separate window

Figure 4

Perturbations in DC populations during EVD.

(A) mDCs are lost during the acute phase of disease but return in convalescence. Quantitation of these DCs as a percentage of CD45⁺CD66^– leukocytes is shown in graphs (range for all controls in gray). (B) Coinciding with the return of mDCs in convalescence, CD1c expression on mDCs is dramatically upregulated. (C) pDCs have increased expression of CD38 during the acute-phase time points. Samples from a control obtained on 3 separate dates are shown for comparison for both B and C.

pDC frequencies were elevated above control levels only in EVD9 (Figure 4C), and only at the first time point. Perhaps the marked increase in pDCs in this patient was related to the severity of disease, as at that time point, the patient had all of the clinical, virologic, and laboratory indicators of a fatal outcome. Alternatively, elevated numbers of pDCs could have been secondary to innate immune stimulation of the experimental therapeutic (siRNA) that they received. While not all patients exhibited an absolute increase in pDC numbers, in 3 of the 4 patients, CD38 was upregulated on pDCs at the earliest time point. Importantly, pDCs were observed at all time points and were activated, as evidenced by the expression of the activation marker CD38.

SCAFFoLD maps of cell abundance identify unique cell populations that are expanded during the acute phase of infection.

Multiparameter analyses can also be visualized with an unsupervised approach that can identify similarities and differences in cell populations that are not revealed by traditional gating. Single-cell analysis by fixed force- and landmark-directed (SCAFFoLD) (45) maps were created to illustrate the abundance of various cell populations at the acute versus convalescent time point for each patient (Figure 5A). For EVD9, the time points of day 14 and day 28 were chosen because these were most similar to the time points available from the other patients in previous analyses (phenotypic properties and viral control). Most cells were of lower abundance during the acute phase of disease. However, select cell populations were more abundant during the acute phase, most notably near the monocyte or CD8⁺ T cell cluster. For example, cluster 41, seen in increased abundance in all 4 patients, is a Lin^–CD11b⁺Ki-67⁺pSTAT1⁺ population. Cluster 98, in high abundance in the acute phase in 3 of the 4 patients, is CD4⁺CD8⁺Ki-67⁺pSTAT1⁺. Double-positive T cells have been reported in other infectious disease processes (46), although their significance is unclear. SCAFFoLD maps of the Ki-67 signal were also generated for all 4 patients (Figure 5B), demonstrating that despite the low abundance of many cell populations at the acute phase, broad, active proliferation also occurred, which may have contributed to normalization of cell numbers over time.

Open in a separate window

Figure 5

SCAFFoLD maps of cell abundance at acute phase versus convalescent phase.

Fold change in cell abundance (A) and differences in median Ki-67 signal (B) at an acute versus a convalescent time point for each patient. Most cell populations, including monocytes, NK cells, and T cells, decrease in abundance during the acute phase but show active proliferation.

Monocyte loss and repopulation.

The dramatic loss of intermediate and nonclassical monocytes was striking in all 4 patients during the acute phase, in line with prior observations of monocyte loss in patients with EVD (2). EBOV infects monocytes in vitro, so it is possible that these cells were lost to infection-related cell death. However, CD16⁺ monocytes are known to play a unique role in patrolling and responding to inflammation, so they may be lost from blood due to recruitment to the inflamed tissues (47). While the full function of intermediate monocytes has not been clearly defined, they are believed to play a role in T cell proliferation and stimulation, inflammatory responses, and presentation of antigen (47). Recently, a similar phenomenon was reported to occur during dengue virus infection: intermediate monocytes were lost and then repopulated via upregulation of CD16 on classical monocytes (48). Our finding that intermediate monocytes repopulate first, followed by nonclassical monocytes after marked classical monocyte proliferation, would be consistent with that phenomenon occurring during EVD. Notably, a study performed in humans infected with Chikungunya virus (49) made use of mass cytometry to define and characterize the changes that occur during acute infection and recovery; an expansion of the intermediate monocyte population during the acute phase was observed. Whether this represents differences in time of sampling or differences related to the etiology of the infection is an area for future study.

Evidence for emergency hematopoiesis during EVD.

Analysis of the data using SCAFFoLD maps revealed several unique populations of increased abundance in the acute phase. Interestingly, at least one of these populations (cluster 41) has markers (CD11b⁺Ki-67⁺Lin^–) similar to those of the large population of cells identified initially in EVD9 and ultimately noted in all 4 patients but, importantly, not in the controls. We hypothesize that these clusters may represent cells of myeloid origin that have egressed from the bone marrow during a time of emergency hematopoiesis. It is well established that physiologic insult, including infection, can lead to expansion of hematopoietic stem cells and myeloid progenitors in order to control an ongoing infection (50, 51). This is an important mechanism for myeloid expansion during inflammation, since, unlike for lymphoid cells, there is no receptor specificity to drive proliferation. Instead, growth factors, such as M-CSF, GM-CSF, and G-CSF, are produced and stimulate the bone marrow to increase myelopoiesis. Indeed, during EVD, all 3 growth factors are produced at higher levels than in normal healthy controls; furthermore, elevated levels of MCSF were associated with severe manifestations of EVD (8). The Lin^–CD11b⁺ cells that we observe during acute EVD have similarities to a population of cells known as myeloid-derived suppressor cells (MDSCs) that have been described in sepsis (52) and arise as a result of emergency hematopoiesis. In fact, these MDSCs are reported to produce IL-10 (which is also elevated in EVD, especially in fatal cases) and TGF-β to exert immunosuppressive effects. In many cases of fatal EVD, patients die from the disease before generating significant adaptive immune responses, and the immunosuppressive milieu generated by MDSCs could explain these findings. The clinical course of patient EVD9 mirrors this: the patient had very high levels of MCSF from 6 to14 days after symptom onset accompanied by high levels of IL-10 during the same time period as noted in a prior publication (8). This also coincides with the high frequency of the Lin^–CD11b⁺ cell population, which peaked on day 14 and constituted 17% of the total non-neutrophil CD45⁺ events; by day 20, this population decreased to 1.6% (it was less than 0.5% in controls). These findings suggest that, at least in cases of severe EVD, emergency hematopoiesis could be activated.

Sample processing.

Whole blood collected in EDTA tubes was centrifuged 500 g for 5 minutes, and plasma was removed. Packed RBCs and leukocytes were suspended in an equal volume of PBS and layered on top of an equal volume of Ficoll 1077 (MilliporeSigma). Following centrifugation at 500 g for 30 minutes without a brake, the PBMC layer was collected and washed in PBS. Following another centrifugation at 500 g for 5 minutes, the PBMC pellet was suspended in freezing media (90% FBS, 10% DMSO) and aliquoted to cryovials at an equivalent of 2 mL whole blood per tube.

Processing for mass cytometry.

For mass cytometry analysis, cryopreserved PBMC samples were thawed and washed in PBS. For experimental samples, the entire tube of PBMCs (equivalent to 2 mL whole blood) was used. For control samples, 2 × 10⁶ PBMCs were used. To label dead cells, samples were incubated for 5 minutes with 200 μL Cell-ID Cisplatin (Fluidigm) diluted 1:1000 in PBS. Samples were then washed in cell staining medium (CSM) consisting of low-barium PBS with 0.5% BSA and 0.02% sodium azide and barcoded using a Cell-ID 20-Plex Pd Barcoding Kit (Fluidigm). Samples were treated with 1 mL of 1× Maxpar Fix I Buffer for 10 minutes at room temperature (RT), then washed twice in 1 mL of 1× Maxpar Barcode Perm Buffer. Each sample was then incubated with a unique barcode for 30 minutes at RT. Samples were washed twice in 1× Maxpar Cell Staining Buffer, suspended in CSM, combined into a single tube, and filtered through a 70-μm filter to remove debris. Human TruStain FcX (BioLegend) was added (2.5 μL per sample) and incubated for 10 minutes at RT. Surface stains (see Supplemental Table 1 for antibodies used) were then directly added and incubated for 30 minutes at RT with periodic rotation. Samples were washed twice in CSM before the cell pellet was incubated in BD Fix/Perm for 20 minutes at RT. At this point, the samples were removed from BSL-4 containment. Samples were washed twice in CSM; pellets were suspended in 100% ice-cold methanol and stored overnight at –80°C. The following day, samples were washed 3 times in CSM and incubated with intracellular stains (see Supplemental Table 1) for 1 hour at RT. Samples were then washed in CSM and incubated for 20 minutes at RT in an 191/193 Ir DNA Intercalator (DVS Sciences) diluted 1:5000 in PBS with 1.6% paraformaldehyde. Samples were washed 3 times in MilliQ water, then suspended in 0.1× EQ beads (Fluidigm) to a concentration of 1000 stained cells/μL. Events were acquired on a CyTOF 2 Mass Cytometer (Fluidigm).

Data processing.

FCS files obtained from CyTOF were normalized (57) and de-barcoded (58) using standard protocols, and analyzed in CellEngine (Primity Bio). A representative gating strategy (from a single donor) used to identify cell populations is shown in Figure 1. Frequencies of each cell population were determined as a function of total CD45⁺CD66^– events (live, singlet, non-RBC, and non-platelet). Exported frequencies were plotted in Prism (GraphPad) to generate graphs shown in Figures 2–4. Median signal intensity data for all intracellular signaling markers (phospho-antibodies) were exported to Excel (Microsoft). Fold difference in the experimental sample versus the average median of simultaneously acquired control samples was determined, and the data were plotted in heatmap format using Prism to show each patient’s time points and all of the measured cell types (Supplemental Figure 1).

For SCAFFoLD (34) analysis, the CD45⁺CD66^– population was exported as.fcs files and subsampled to 19,000 events per donor and time point (the greatest common denominator among all FCS files). Events were pooled and designated to one of 200 clusters based on their surface marker expression. Landmark nodes were defined by known, gated cell populations from control donors.

Due to the limited sample size, statistical analysis was inappropriate and was not performed.

Study approval.

The patients (2 female, 2 male) were cared for in the Emory Serious Communicable Diseases Unit. IRB approval was obtained from both Emory University (IRB0076700) and the CDC (IRB6643) for the reported studies. PBMCs were obtained from healthy control donors via an approved IRB protocol (CDC1652). All study participants provided written informed consent and were assigned an EVD number. These same designations were used in prior publications (8, 23).

We would like to thank the patients for their willingness to participate and acknowledge the medical staff of the Emory University Serious Communicable Diseases Unit for providing excellent care to the patients. We thank Tanya Klimova for editing the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention or the US Food and Drug Administration. AK McElroy was supported by a Burroughs Wellcome award (no. 1013362.01) and an NIH K08 grant (AI119448) during the course of this work. RSA was supported by Cooperative Centers on Human Immunology (CCHI) Opportunity funding 2016–2017. ZBH, DRM, HC, NM, GN were supported by US Food and Drug Administration contracts HHSF223201610018C and HHSF223201210194C. The Defense Advanced Research Projects Agency (W31P4Q-14-1-0010) partially supported this work.