Quantitative Imaging Analysis of the Spatial Relationship between Antiretrovirals, Reverse Transcriptase Simian-Human Immunodeficiency Virus RNA, and Fibrosis in the Spleens of Nonhuman Primates
ABSTRACT
Although current antiretroviral therapy (ART) has increased life expectancy, a cure for human immunodeficiency virus (HIV) remains elusive due to the persistence of the virus in tissue reservoirs. In the present study, we sought to elucidate the relationship between antiretrovirals (ARVs) and viral expression in the spleen. We performed mass spectrometry imaging (MSI) of 6 different ARVs, RNAscope in situ hybridization of viral RNA, and immunohistochemistry of three different fibrosis markers in the spleens of 8 uninfected and 10 reverse transcriptase simian-human immunodeficiency virus (RT-SHIV)-infected rhesus macaques (infected for 6 weeks) that had been dosed for 10 days with combination ART. Using MATLAB, computational quantitative imaging analysis was performed to evaluate the spatial and pharmacological relationships between the 6 ARVs, viral RNA, and fibrotic deposition. In these spleens, >50% of the spleen tissue area was not covered by any detectable ARV response (any concentration above the limits of detection for individual ARVs). The median spatial ARV coverage across all tissues was driven by maraviroc followed by efavirenz. Yet >50% of RNA-positive cells were not exposed to any detectable ARV. Quantifiable maraviroc and efavirenz colocalization with RNA-positive cells was usually greater than the in vitro concentration inhibiting 50% replication (IC50). Fibrosis markers covered more than 50% of the spleen tissue area and had negative relationships with cumulative ARV coverages. Our findings suggest that a heterogeneous ARV spatial distribution must be considered when evaluating viral persistence in lymphoid tissue reservoirs.
INTRODUCTION
For over 3 decades, the mainstay for the treatment of human immunodeficiency virus (HIV) is antiretroviral (ARV) therapy (ART) (1). Although combination ART has resulted in undetectable plasma viral loads (pVLs) and an increased life expectancy comparable to those of individuals living without HIV, there is not yet a cure (2). Moreover, lifelong adherence to daily ART is required, as plasma viral rebound occurs after treatment disruption.
The major barrier to a cure for HIV is the persistence of HIV reservoirs in the body’s tissues (3–5). The primary tissues in which reservoirs reside are secondary lymphoid tissues, including the gut-associated lymphoid tissue (GALT), lymph nodes (LNs), and the spleen (5, 6). Even though the spleen is an important secondary lymphoid tissue in the immune system, it also plays an important role in the circulatory system (7). Because of the spleen’s unique dual primary functions, the biology of HIV reservoir persistence and composition may be unique within the spleen compared to other secondary lymphoid tissues.
While the hypothesis of ongoing viral replication within lymphoid tissues was put forth, this being the cause of reservoir maintenance remains controversial (8). Nolan et al. noted that the spleens from 5 participants virally suppressed on ART at the time of death contained clonally expanding cells with identical proviral genomes, highlighting the importance of the spleen tissue (9), and recently, Bozzi et al. did not detect evidence of genetic changes in HIV sequences, in either blood or tissues, in patients after years of ART, indicating growing evidence toward clonal expansion as the primary driver of reservoir maintenance (10). In light of this controversy, it was previously suggested that the persistence of long-lived infected cells may be related to limited exposure to ARVs (8, 11). This is imperative as experimental cure strategies, such as “kick-and-kill,” will require adequate ART concentrations within deep-tissue reservoirs to prevent viral persistence (12).
Historically, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been the gold standard for quantifying ARV concentrations in biological matrices. Our group previously reported ARV concentrations of homogenized spleens from nonhuman primates (NHPs) and humans and noted large variations in splenic concentrations compared to those in plasma (7.5-fold lower to 9.5-fold higher, depending on the drug) (13). But sample preparation, specifically whole-tissue homogenization, results in the loss of important spatial information. Elucidating the geographical distribution of ARVs within tissues is an important next step to study the pharmacological efficacy of drugs (6). Indeed, novel mass spectrometry imaging (MSI) methods in the gut, brain, and LNs have illuminated the heterogeneous ARV distribution within these tissue reservoirs (14–16).
Analyses of splenic inflammation and fibrosis in those living with HIV are limited. Histological findings within postmortem spleens of virally suppressed HIV-positive (HIV+) patients include inflammation and lymphoid hyperplasia (17). Given that the spleen is a fibrous organ (18), additional fibrosis may provide a barrier to drug diffusion and distribution. However, the influence of fibrosis on the splenic drug distribution is unknown.
Here, we utilize a novel MSI method known as infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) to image 6 ARVs, RNAscope in situ hybridization (ISH) to image viral RNA (vRNA), and immunohistochemistry (IHC) for fibrosis markers in the spleens of uninfected NHPs and NHPs infected with reverse transcriptase simian-human immunodeficiency virus (RT-SHIV). Quantitative computational imaging analysis methods were employed to understand the spatial relationships between these important drug treatment factors.
(This work has been presented in part at the 22nd International Workshop on Clinical Pharmacology of HIV, Hepatitis and Other Antiviral Drugs [19]).
RESULTS
MSI analysis of ARVs in spleens.
As described in Materials and Methods, a total of 17 NHPs were included for analysis in this study. There had been no (0%) LC-MS/MS concentrations below the limit of quantification (BLQ). As indicated in Table 1, spleen ARV concentrations, as determined by quantitative MSI, were modestly correlated with LC-MS/MS ARV concentrations and reached statistical significance only for efavirenz (EFV) (P = 0.03). Correlation plots for the individual ARVs are displayed in Fig. S1 in the supplemental material. Compared to LC-MS/MS, MSI ARV concentrations were approximately 40% lower. Given the large numbers of BLQ values by MSI quantification, subsequent quantitative MSI analyses for emtricitabine (FTC) and raltegravir (RAL) were not performed. Figure 1 displays representative images of the intensity-scaled and tissue-specific MSI images of the distribution for NHP 42707, an RT-SHIV+ NHP treated with the regimen containing FTC, tenofovir (TFV), EFV, and RAL. Heme was measured on 37% of this tissue, similar to its presence on 40% (range, 17 to 67%) of all tissues. All analyses focused on tissue-specific ARV concentrations to ascertain ARV concentrations in tissue versus the vascular space. To visualize the tissue-specific response, Table 2 reports the percentages of the spleen area that registered a drug response, both total and tissue specific. The tissue-specific cumulative (whole-regimen) drug response was a median of 42% (range, 2 to 80%) of the spleen tissue area. When focusing on the tissue-specific response, drug concentrations were reduced by approximately 20%, although this was not statistically significant. Of all ARVs, maraviroc (MVC) had the highest coverage across spleen tissues (median of 71%), with most concentrations exceeding the in vitro concentration inhibiting 50% replication (IC50) value. ARV distribution patterns were heterogeneous, as indicated by dynamic ranges (DRs) summarized in Table S4 in the supplemental material.
FIG 1
TABLE 1
| ARV | No. of NHPs (no. of NHPs with concn BLQ for MSI) | Median LC-MS/MS concn (ng/g of tissue) (range) | Median MSI concn (ng/g of tissue) (range) | Rho | P |
|---|---|---|---|---|---|
| FTC | 17 (14) | 27.9 (6.5–45.2) | 31.3 (1.5–60.2) | −0.5 | 1 |
| TFV | 17 (3) | 261 (62–632) | 137 (1.5–1,650) | 0.3 | 0.2 |
| EFV | 8 (1) | 505 (3.8–1,320) | 433 (20.8–2,509) | 0.8 | 0.03 |
| RAL | 8 (5) | 33.9 (4.9–71.3) | 15.4 (12.7–30.6) | −1 | 0.3 |
| MVC | 9 (1) | 281 (3.7–429) | 64.7 (0.6–350) | 0.7 | 0.1 |
| ATV | 9 (3) | 17.9 (7.0–27.2) | 18.2 (7.0–605) | −0.7 | 0.2 |
a
Data are medians (ranges). Mass spectrometry imaging (MSI) concentrations that were below the limit of quantification (BLQ) were excluded from this analysis. Correlations (rho) and associated P values were calculated by nonparametric Spearman’s rank correlation, excluding BLQ values. ARV, antiretroviral; FTC, emtricitabine; TFV, tenofovir; EFV, efavirenz; RAL, raltegravir; MVC, maraviroc; ATV, atazanavir.
TABLE 2
| ARV | No. of NHPs | Median % spleen area coverage (range) | P | No. of NHPs used to evaluate per-voxel concn | Median % tissue-specific ARV area > IC50 (range) | |
|---|---|---|---|---|---|---|
| Total | Tissue specific | |||||
| FTC | 17 | 0.7 (0.06–45.1) | 0.7 (0.05–39.4) | 0.7 | 3 | — |
| TFV | 17 | 0.6 (0.2–34.5) | 0.5 (0.2–21.3) | 0.5 | 14 | 57.5 (0–100) |
| EFV | 8 | 15.4 (0.1–96.2) | 9.2 (0.1–85.7) | 0.8 | 7 | 100 (100–100) |
| RAL | 8 | 0.3 (0.1–1.1) | 0.3 (0.1–1.1) | 0.5 | 3 | — |
| MVC | 9 | 80.1 (1.7–91.8) | 71.5 (1.6–79.8) | 0.2 | 8 | 100 (100–100) |
| ATV | 9 | 0.8 (0.4–53.4) | 0.7 (0.3–33.4) | 0.4 | 6 | — |
| Cumulative | 17 | 51.0 (2.9–97.2) | 42.2 (2.1–87.6) | 0.3 | ||
a
Data are medians (ranges). Dashes indicate that the low per-voxel sensitivities of emtricitabine (FTC) and raltegravir (RAL) in the spleen tissue precluded per-voxel quantitative analysis to calculate the percentages of the tissue-specific area above the respective in vitro concentrations inhibiting 50% replication (IC50s). P values were calculated via a Wilcoxon rank sum test. ARV, antiretroviral; TFV, tenofovir; EFV, efavirenz; MVC, maraviroc; ATV, atazanavir.
Examining the coverage of ARVs in spleen tissue slices, in all 17 NHPs, a median of 97% of volumetric pixels (voxels) with detectable drug were due to only one ARV (Fig. 2). Across all NHPs, these ARVs were EFV or MVC, dependent on the regimen. There was a sharp decline in the number of voxels that had more than one drug. Sixteen spleens had 2 ARVs colocalized, but this accounted for less than 5% of the tissue area. Twelve NHPs had 3 ARVs colocalized, accounting for only 0.3% of the tissue area, and 4 NHPs had all 4 ARVs colocalized in a very small percentage of the tissue area, ranging from 0.3 to 3.6%. Overall, across the NHPs, EFV or MVC contributed approximately 99% of the detectable total cumulative coverage.
FIG 2
Cell-associated RNA and colocalization with ARVs.
Representative images of cell-associated RNA (caRNA) and detectable drug concentrations are shown in Fig. 3. The cell-associated RNA viral load was 7.1 copies/mm2 of tissue (range, 1.6 to 28.6 copies/mm2) (see Table S5 in the supplemental material), which amounted to 0.002% (range, 0.002 to 0.004%) of the tissue area. ARVs were found to colocalize with caRNA cells in only 5 of 9 NHPs, driven by EFV (46%) and MVC (14%), as shown in Table 3. RAL and atazanavir (ATV) were not found to colocalize with any vRNA+ infected cells. Of note, the follicular dendritic cell (FDC)-trapped virions were predominantly located in the B cell follicles (BCFs), as shown in Fig. S2 in the supplemental material.
FIG 3
TABLE 3
| ARV | No. of infected NHPs | No. of NHPs with ARV-covered cell-associated virus | Median % of cell-associated virus response covered by ARV (range) |
|---|---|---|---|
| FTC | 9 | 1 | 0 (0–35.7) |
| TFV | 9 | 1 | 0 (0–7.1) |
| EFV | 4 | 2 | 46.4 (0–100) |
| RAL | 4 | 0 | 0 (0–0) |
| MVC | 5 | 4 | 14.2 (0–100) |
| ATV | 5 | 0 | 0 (0–0) |
| Cumulative | 9 | 5 | 44.4 (0–100) |
a
Data are medians (ranges). ARV, antiretroviral; FTC, emtricitabine; TFV, tenofovir; EFV, efavirenz; RAL, raltegravir; MVC, maraviroc; ATV, atazanavir.
Quantitative imaging analysis of fibrosis markers with ARVs.
Fibrosis markers covered 62% (range, 49 to 76%) of the downsampled tissue area. Between the uninfected and RT-SHIV+ NHPs, there was no significant difference in the percent coverages of any marker. Correlations between fibrosis marker coverage and individual ARVs were not statistically significant, but there was a negative relationship between the total tissue area covered by ARVs and the total tissue area covered by fibrosis markers (see Fig. S3 in the supplemental material). Only collagen type 3 had a significant negative relationship with the total ARV distribution (rho = −0.5; P = 0.03).
DISCUSSION
This study explored the spatial relationship of ARVs, RT-SHIV RNA, and fibrosis markers in the spleens of NHPs using multiple quantitative imaging modalities. The ability to detect FTC and RAL proved to be particularly challenging by MSI in spleen tissue, where the limits of detection were >10 times their respective IC50 values. Therefore, these were excluded from calculations related to the percent areas above the inhibitory concentrations in Table 1. We noted that for spleen tissue, approximately 20% of the drug response measured was associated with heme and was likely associated with blood. After isolating the ARV distribution within tissues, the drug distribution was still noted to be diffused throughout the tissue, and the extent of diffusion was drug dependent. Approximately 50% of drug concentrations were above the IC50 estimates for TFV, and nearly 100% were above the IC50 estimates across all ARVs. When evaluating TFV, EFV, and MVC, drug detection colocalized with <50% of caRNA. However, when detected, these concentrations were above the IC50 values. Finally, while we found a relationship between higher fibrosis marker concentrations and lower drug concentrations for EFV, fibrosis markers may not independently explain low ARV coverage overall, at least at these early time points postinfection.
Previous LC-MS/MS spleen tissue analyses in these NHPs had shown that the three highest ARV concentrations came from MVC, EFV, and TFV (13). This aligns well with our rankings of the percentages of the spleen tissue area occupied by these ARVs using IR-MALDESI as well as data observed for the mesenteric LNs from these same NHPs (16). The imaging analyses also demonstrated that these ARV distributions were spread diffusely throughout the spleen and were not necessarily localized to one morphological region. The degree of heme association observed in this study is not surprising given the spleen’s role in erythrophagocytosis and iron metabolism (7).
The RNAscope analyses permitted us to identify where FDC-trapped virions and caRNA are located within the spleen tissue. As expected, virions were predominantly located within the B cell follicles, where they would be trapped (20–22). Compared to the LNs from these same NHPs, the caRNA viral load was approximately 10-fold lower in the spleen (16). This difference had been observed in a previous 26-week longitudinal study of simian immunodeficiency virus (SIV)-infected NHPs, where the spleen’s contribution to the total viral burden was <1% before and after chronic ART (23). Given that the primary site of infection is within the secondary lymphoid tissue portion of the spleen (e.g., the white pulp), it is conceivable that after normalization to the white pulp tissue only, more similarities to the LNs could be seen. We did note a significant relationship between the size of the FDC reservoir and the caRNA viral load (rho = 0.8; P = 0.01). This aligns with previous findings that demonstrated the correlation of the size of the FDC reservoir with the number of productively infected cells within lymphoid tissues (21).
The findings of incomplete coverage of ARVs with caRNA are similar to our findings with gastrointestinal (GI) tract tissue (both ileum and rectum) and mesenteric LNs from these same NHPs (14, 16) and add to the growing evidence that a heterogeneous ARV distribution contributes to viral persistence and expression in reservoir tissues. However, in all of these other tissues, nearly 80% of the caRNA was colocalized with at least one ARV, at concentrations exceeding the IC50 values (14). Furthermore, large differences in caRNA viral loads seen in the present study in the spleen compared to other known tissue reservoirs were demonstrated previously (12, 16, 23–26).
Previous studies hypothesized the role of fibrosis in potentially limiting drug disposition in lymphoid tissues, especially in LNs (27, 28). Through IHC imaging and quantitative analysis, over 50% of NHP spleen tissues were occupied by one of the three fibrosis markers studied. Contrary to previous data for mesenteric LNs from these same NHPs (15), no statistical differences in fibrosis marker concentrations were noted between the spleens of uninfected and those of RT-SHIV+ NHPs. However, given that the spleen is a highly fibrous organ at baseline due to the extensive and fibrillar white pulp region, detectable increases in fibrosis may be minimal over this short period of infection (7 weeks) in this study (7). Since the viral burden was also lower than those in other tissues, there would reasonably be less immune activation and lower increases in transforming growth factor β1 (TGF-β1) signaling, which has been previously shown to correspond to collagen deposition (28–30). Nonetheless, we observed a negative relationship between the amount of collagen type 3 and the total ARV tissue distribution from the MSI approach (rho = −0.5; P = 0.03), lending credence to the hypothesis that for some drugs, fibrosis may limit drug distribution.
The spleen may not be as large of a viral reservoir as GALT or LNs, and this might be due to the cellular protective role of the intracellular metabolites of FTC and TFV. Both parent compounds are biotransformed into their active metabolites (FTC triphosphate [FTCtp] and TFV diphosphate [TFVdp]) via intracellular phosphorylation. Devanathan et al. previously showed that the splenic molar percentages of FTCtp and TFVdp in these spleens, as measured by LC-MS/MS, were 22.3% and 80.3%, respectively (13). In the LNs and ileal and rectal tissues, these percentages are approximately 5% (31, 32). In the present study, MSI FTC and TFV concentrations were low, and coverage across the tissue was minimal. Therefore, because the active metabolites may be more pharmacologically prevalent, these higher intracellular concentrations could have provided a greater magnitude of cellular protection against viral infection within the spleen. As the sensitivity of the MSI technology precluded the examination of the distribution of the phosphorylated metabolites, this warrants future investigation.
Recently, within the context of HIV cure research, nonnucleoside reverse transcriptase inhibitors (NNRTIs) have demonstrated pyroptotic activity by increasing the intracellular processing of Gag and Gag-Pol within MT4, a transformed T cell line, in a concentration-dependent manner; 50% cytotoxicity occurred at EFV concentrations of approximately 1.71 μM (~540 ng/mL) (33). This was further demonstrated in resting and activated primary CD4 T cells, with 50% cell death being observed at concentrations of ~500 ng/mL (34). In the 4 infected NHPs here, the median EFV concentrations determined via MSI and LC-MS/MS were 66 and 183 ng/g, respectively. Interestingly, in our tissues, the number of infected cells detected displayed a negative relationship with percent EFV coverage across the spleen tissue (rho = −0.8; P = 0.2) (data not shown). Further exploration of this finding with tissue-resident cells (e.g., CD4 T cells within the spleen) is warranted.
There are limitations to this study. First, these analyses represent a single point in time (10 days following ART); therefore, it is difficult to extrapolate the tissue ARV distribution across a dosing interval, specifically at the times of maximum concentrations, to elucidate dynamic diffusion processes. However, longitudinal sampling across a dosing interval would require a large number of animals, which would be technically challenging to implement. Second, a tissue slice, while 10 μm thick, represents only one two-dimensional examination of a three-dimensional organ. It is conceivable that different sections may have different viral RNA responses and extents of fibrosis. To mitigate this, our measures utilized sections in the middle of the spleens of NHPs to account for the potential heterogeneity in responses.
Third, we found lower IR-MALDESI sensitivity to ARVs in spleen tissue than in other tissues from these same animals. Tissue matrix effects were particularly challenging due to either analytical interference from the high blood plasma content of these tissues or a higher endogenous response in the spleen tissue than in other tissues. Therefore, the limits of detection for FTC and RAL, in particular, tended to be higher than their respective IC50 values and precluded quantitative analyses. Fourth, the resolution of MSI images (100 μm/pixel) is lower than that of IHC/ISH images (0.5 μm/pixel) (35, 36). To overlay and coregister MSI and IHC/ISH images, it was necessary to match all images to the resolution of the MSI data. While this likely overestimates the degree of colocalization, this represents the “best-case” scenario, and any lower colocalization would further confirm the hypothesis of a heterogeneous distribution and viral persistence. Nevertheless, the process of downsampling provides novel visualizations of ARVs alongside ISH and IHC images. Further optimization of the sensitivity and resolution of IR-MALDESI is ongoing.
Due to sample limitations, we had not performed optimization and staining for macrophages within the spleen tissues. However, there is growing evidence that has highlighted the importance of latently infected macrophages that contribute to the HIV reservoir (37–39). The spleen, alongside the blood and lungs, contains the majority of latently infected macrophages (39). Further studies that quantify ARV concentrations within blood and tissue macrophages would elucidate the potential for differential and/or inadequate penetration into these important cell types. Finally, cells within tissues are not static but are in a constant state of migratory flux; thus, cells likely enter and exit locations with variable ARV levels but cumulatively may encounter sufficient levels to be highly effective in vivo.
In conclusion, we demonstrated the utility of combinatorial imaging modalities to evaluate ARV, viral RNA, and fibrosis distributions in the spleens of NHPs to test the hypothesis that a heterogeneous ARV distribution permits the persistence of the virus. We showed that the ARV distribution is heterogeneous in the spleen tissue but not necessarily caused by fibrosis this early in infection. Yet certain drugs and tissues may be more sensitive to the effects of fibrosis than others. Interestingly, <50% of RT-SHIV virions and infected cells were exposed to any detectable ARV, suggesting that there may be a pharmacological rationale for viral persistence in the spleen. These data have implications not only for drug therapy but also for the development of kick-and-kill cure strategies, which rely on the premise of protective ARV coverage of uninfected cells during latency reversal in lymphoid tissue reservoirs such as the spleen.
MATERIALS AND METHODS
NHP study and tissue collection.
The NHP study and tissue collection were described previously (13). Briefly, 18 rhesus macaques (Macaca mulatta) were used in this study. Eight NHPs (6 males and 2 females) were left uninfected, and 10 NHPs (6 males and 4 females) were infected for 6 weeks with RT-SHIV. The pVL was monitored during the infection period. After 6 weeks, all animals were dosed to steady state (10 days) with ARV doses based on previously reported effective treatment regimens in this model. All NHPs received daily subcutaneous doses of 30 mg/kg of body weight of emtricitabine (FTC) and 16 mg/kg of body weight of tenofovir (TFV) plus one of the following oral combinations: 200 mg of efavirenz (EFV) daily plus 100 mg of raltegravir (RAL) twice daily (FTER regimen) (n = 9) or 150 mg/kg of maraviroc (MVC) twice daily plus 270 mg/kg of atazanavir (ATV) twice daily (FTMA regimen) (n = 9). One day after the final dose, NHPs were euthanized using phenobarbital, and necropsy was performed. At necropsy, spleens were collected from each NHP and snap-frozen with liquid nitrogen. Plasma and spleen tissue viral loads were measured using previously reported methods (40, 41). All relevant viral load measures are reported in Table S1 in the supplemental material. Spleen tissue was stored at −80°C until analysis. NHP 40422, on the regimen containing FTC, TFV, EFV, and RAL, developed liver failure; this NHP was excluded from further analysis.
Tissue sectioning.
The spleens from each NHP (n = 18) were mounted onto a specimen disc using O.C.T. (Tissue-Tek; Sakura Finetek, Inc., Torrance, CA) and cryosectioned at −16°C using a Leica CM1950 cryomicrotome (Leica Biosystems, Buffalo Grove, IL) at a thickness of 10 μm. Sections were thaw-mounted onto positively charged glass slides (immunohistochemistry [IHC] and ISH) or plain glass slides (IR-MALDESI). Serial sections were collected from each spleen in the following order (and quantity) for each imaging method: IHC (3 sections), IR-MALDESI and LC-MS/MS (6 sections [alternating]), and ISH (2 sections).
LC-MS/MS and IR-MALDESI MSI analyses.
LC-MS/MS methods for spleen tissue homogenate samples were detailed previously (13). The workflow for IR-MALDESI MSI analyses was described in greater detail by our group previously (16) but can also be found in the supplemental material. Using MATLAB, the per-volumetric pixel (voxel) drug response was quantified based on calibration on matched blank spleen tissue (42). Utilizing a volumetric pixel area of 0.1 mm2, a section thickness of 0.01 mm, and a tissue density of 0.00106 g/mm3, concentrations were converted to units of nanograms per gram of tissue. MSI measures both drug and endogenous information simultaneously, including a marker for blood (heme). Individual and cumulative (i.e., total ARV responses from all 4 drugs) ARV maps are generated. Tissue-specific ARV concentrations can be evaluated by correcting for heme. The lower limits of quantification for LC-MS/MS (in nanograms per gram of tissue) were 0.002 for FTC and MVC; 0.005 for EFV, RAL, and ATV; and 0.02 for TFV. The limits of detection for the MSI approach (in nanograms per gram) were 110 for MVC, 130 for ATV, 170 for EFV, 1,160 for TFV, 1,600 for FTC, and 3,040 for RAL. It is important to note that these limits of detection relate to the signal abundance of individual pixels in the image. Because the response to a drug is not uniform across the tissue, the average tissue concentration may be lower than the limit of detection (43). The heterogeneity of the ARV response has been evaluated by two approaches: (i) the proportion of the total tissue area with detectable drug and (ii) the dynamic range (DR) of the drug response where detectable.
The IC50 values used in these analyses (in nanograms per gram) were 2.2 for MVC (44), 2.9 for RAL (45), 11.9 for EFV (46), 16.6 for ATV (47), 149 for FTC (48), and 2,303 for TFV (48). To quantify intratissue ARV heterogeneity, the unitless DRs were calculated as follows: 10 × log10(maximum/minimum intensities) (49). This information is recapitulated in Table S2 in the supplemental material.
RNAscope ISH analysis and multiplex IHC.
ISH analysis of viral RNA was performed on splenic sections using a modification of a previously reported RNAscope protocol (16, 20). Specifically, frozen tissue sections on slides were brought to room temperature, dried for 15 min, incubated for 2 h in a 4% paraformaldehyde (PFA) fixative (catalog number 15714-S; Electron Microscopy Sciences) in phosphate-buffered saline (PBS) at room temperature, incubated in 100% ethanol for 5 min twice, and then rinsed once in double-distilled water (ddH2O). Heat-induced epitope retrieval was performed as follows: slides were boiled in 1× Dako target retrieval solution (pH 9) (catalog number S236784-2; Agilent) for 5 min, immediately transferred to PBS, and incubated in peroxidase blocking solution (3% H2O2 diluted in 1× PBS) for 10 min at room temperature, followed by a rinse in ddH2O.
Slides were incubated with the target probe SIVmac239 antisense (catalog number 314071; ACD) for 2 h at 40°C, and amplification was performed using the RNAscope 2.5 HD brown detection kit (catalog number 322310; ACD) using 0.5× wash buffer (catalog number 310091; ACD) for all washing steps. Slides were developed with Alexa Fluor 647 (AF647)-conjugated tyramide (catalog number B40958; Invitrogen) at a 1:500 dilution for 2 min.
Slides were then stained using rabbit anti-CD4 (catalog number ab133616; Abcam) (0.143 mg/mL) at 1:200 and goat anti-CD20 (catalog number PA1-9024; Invitrogen) (0.5 mg/mL) at 1:200 overnight. The slides were then incubated with secondary donkey anti-goat–AF488 (catalog number A11055; Invitrogen) (2 mg/mL) at 1:200 for 1 h and donkey anti-rabbit–AF755 (catalog number SA5-10043; Invitrogen) (0.5 mg/mL) at 1:200 for 1 h. All staining was performed at room temperature; slides were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) (catalog number D1306; Invitrogen) at a dilution of 0.5 μg/mL in ddH2O for 10 min and coverslipped using Prolong Gold antifade mounting medium, and whole tissues were scanned on an AxioScan.Z1 instrument (Zeiss) using a Plan Apochromat 20× objective (0.8 numerical aperture [NA], FWD = 0.55 mm; Zeiss).
The relative amount of SIV RNA trapped within the FDC network or within infected cells was estimated by quantifying the total SIV RNA signal intensity using the ISH module FISH v1.1 within HALO software (v3.2.1851.393; Indica Labs). We measured the signal intensity (minimum, mean, and maximum) of more than 100 identifiable individual virions within B cell follicles, which corresponds to 2 copies of SIV RNA, and then divided the value by 2 to approximate the signal intensity of a single copy of vRNA. Next, annotation layers were placed around putative SIV-infected cells based on anatomic localization, cell morphology, and CD4 positivity; analysis of the relative quantity of SIV vRNA (>4 copies) confirmed designation as an SIV-infected cell. Concomitantly, SIV vRNAs present with CD20+ BCF germinal centers were annotated as FDC-bound virions based on classical morphology. Finally, the relative SIV RNA copy number within SIV RNA+ cells or within the FDC-bound network was calculated as (signal intensity within SIV RNA+ cells [μm2])/(0.5 × mean signal size for a virion).
IHC analysis of fibrosis markers.
To detect collagen type 3, chromogenic IHC was performed on frozen tissues sectioned at 10 μm. This IHC was carried out using the Leica Bond III Autostainer system. Subjacent slides were dewaxed in Bond dewax solution (catalog number AR9222) and hydrated in Bond wash solution (catalog number AR9590). Heat-induced antigen retrieval for collagen type 3 was performed for 20 min at 100°C in Bond epitope retrieval solution 2 (pH 9.0) (catalog number AR9640). The antigen retrieval step was followed by a 5-min Bond peroxide blocking step (catalog number DS9800). After pretreatment, slides were incubated for 1 h with collagen type 3 (1:25) for 30 min. Antibody detection with 3,3′-diaminobenzidine (DAB) was accomplished using the Bond Intense R detection system (catalog number DS9263) supplemented with Novolink post-primary and Novolink polymer secondary antibodies (catalog number RE7260-K; Leica) or with ImmPRESS horseradish peroxidase (HRP) anti-rat IgG (catalog number MP-7444-15; Vector Labs). Stained slides were dehydrated and coverslipped with Cytoseal 60 (catalog number 8310-4; Thermo Fisher Scientific). Positive and negative controls (no primary antibody) were included for each assay. Stained IHC slides were digitally imaged using the Aperio ScanScope XT system (Leica) with a 20× objective.
For collagen type 1 and fibrinogen, sequential dual immunofluorescence (IF) was carried out on the Bond fully automated slide staining system (Leica Microsystems, Inc., Norwell, MA) using the Bond research detection system kit (catalog number DS9455; Leica). Slides were deparaffinized in Leica Bond dewax solution (catalog number AR9222), hydrated in Bond wash solution (catalog number AR9590), and sequentially stained for collagen type 1 and then fibrinogen. Specifically, antigen retrieval for collagen type 1 was performed for 20 min at 100°C in Bond epitope retrieval solution 2 (pH 9.0) (catalog number AR9640). After pretreatment, slides were incubated for 2 h with collagen type 1 antibody (1:100) followed by the Novocastra post-primary and Novolink polymer systems (catalog numbers RE7159 and RE7161; Leica) and then tyramide signal amplification (TSA) Cy5 (catalog number FP1117; Akoya Biosciences, Menlo Park, CA). After the completion of collagen type 1 staining, a second round of antigen retrieval was performed for 20 min at 100°C in Bond epitope retrieval solution 1 (pH 6.0) (catalog number AR9961). Slides were then incubated with the fibrinogen antibody (1:1,500 for 2 h) and then the Novolink polymer (catalog number RE7161; Leica) and detected with TSA Cy3 (catalog number FP1046; Akoya Biosciences). Nuclei were stained with Hoechst 33258 (Invitrogen, Carlsbad, CA). The stained slides were mounted with ProLong Gold antifade reagent (catalog number P36930; Life Technologies). The antibodies of interest are listed in Table S3 in the supplemental material.
Quantitative image analysis and colocalization.
The workflow for quantitative imaging analysis of ARVs, fibrosis, and viral RNA was described previously (16) and is described in detail in the supplemental material. Briefly, image analysis and colocalization were performed using MATLAB R2020a (MathWorks, Natick, MA). Final tissue-specific ARV maps generated by IR-MALDESI analyses were coregistered with viral RNA binary images that preserve the areas of the viral RNA by separating the regions from the background (resulting images are known as “masks”) to evaluate colocalization, including IC50-thresholded ARV maps to assess virus colocalization with inhibitory drug concentrations. This process was then repeated between fibrosis markers and ARVs. Due to the differences in resolution between IHC/ISH (~0.5 μm/pixel) and IR-MALDESI (100 μm/pixel), virion, cell-associated RNA (caRNA), and fibrosis marker masks were downscaled to match the resolution and dimensions of the MSI data.
Statistical analysis.
Data are presented as medians (ranges). For these nonparametric data, the Mann-Whitney U test was used for (i) differences in total and tissue-specific individual and cumulative ARV responses and (ii) variance in the extents of collagen type 1, collagen type 3, and fibrinogen coverages between uninfected and RT-SHIV+ spleens. Correlations were assessed via the Spearman rank correlation test for relationships between (i) LC-MS/MS and MSI drug concentrations, (ii) fibrosis and ARV coverages, and (iii) measures of viral load. All statistical tests were performed using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). A P value of <0.05 was considered statistically significant. Initial MSI data processing was performed using the free, open-source MSiReader software (https://www.msireader.wordpress.ncsu.edu/) (50), and quantitative imaging analyses were performed using MATLAB software accessed through a university license (https://www.mathworks.com/products/matlab.html).
ACKNOWLEDGMENTS
This work was supported by the following funding sources: R01 AI111891 to A.D.M.K.; the University of North Carolina at Chapel Hill Center for AIDS Research (P30AI50410); R01 AI149672, R01 AI143411, and R01 DK119945 to J.D.E.; Bill and Melinda Gates Foundation award INV-002704 to J.D.E.; and the Oregon National Primate Research Center (P51 OD011092). The UNC Translational Pathology Laboratory (TPL) is supported in part by grants from the National Cancer Institute (NCI) (5P30CA016080-42), the NIH (U54-CA156733), the National Institute of Environmental Health Sciences (NIEHS) (3P30 EOS010126-17), the University Cancer Research Fund (UCRF), and the North Carolina Biotechnology Center (NCBT) (2015-IDG-1007). A.S.D. is supported by an American Foundation of Pharmaceutical Education predoctoral fellowship. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
We do not have any conflicts of interest to disclose.
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REFERENCES
1.
Palmisano L, Vella S. 2011. A brief history of antiretroviral therapy of HIV infection: success and challenges. Ann Ist Super Sanita 47:44–48.
2.
Samji H, Cescon A, Hogg RS, Modur SP, Althoff KN, Buchacz K, Burchell AN, Cohen M, Gebo KA, Gill MJ, Justice A, Kirk G, Klein MB, Korthuis PT, Martin J, Napravnik S, Rourke SB, Sterling TR, Silverberg MJ, Deeks S, Jacobson LP, Bosch RJ, Kitahata MM, Goedert JJ, Moore R, Gange SJ, North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) of IeDEA. 2013. Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One 8:e81355.
3.
Chun TW, Carruth L, Finzi D, Shen X, DiGiuseppe JA, Taylor H, Hermankova M, Chadwick K, Margolick J, Quinn TC, Kuo YH, Brookmeyer R, Zeiger MA, Barditch-Crovo P, Siliciano RF. 1997. Quantification of latent tissue reservoirs and total body viral load in HIV-1 infection. Nature 387:183–188.
4.
Chun T-W, Nickle DC, Justement JS, Meyers JH, Roby G, Hallahan CW, Kottilil S, Moir S, Mican JM, Mullins JI, Ward DJ, Kovacs JA, Mannon PJ, Fauci AS. 2008. Persistence of HIV in gut-associated lymphoid tissue despite long-term antiretroviral therapy. J Infect Dis 197:714–720.
5.
Chun T-W, Fauci AS. 2012. HIV reservoirs: pathogenesis and obstacles to viral eradication and cure. AIDS 26:1261–1268.
6.
Busman-Sahay K, Starke CE, Nekorchuk MD, Estes JD. 2021. Eliminating HIV reservoirs for a cure: the issue is in the tissue. Curr Opin HIV AIDS 16:200–208.
7.
Mebius RE, Kraal G. 2005. Structure and function of the spleen. Nat Rev Immunol 5:606–616.
8.
Lorenzo-Redondo R, Fryer HR, Bedford T, Kim E-Y, Archer J, Pond SLK, Chung Y-S, Penugonda S, Chipman J, Fletcher CV, Schacker TW, Malim MH, Rambaut A, Haase AT, McLean AR, Wolinsky SM. 2016. Persistent HIV-1 replication maintains the tissue reservoir during therapy. Nature 530:51–56.
9.
Nolan DJ, Rose R, Rodriguez PH, Salemi M, Singer EJ, Lamers SL, McGrath MS. 2018. The spleen is an HIV-1 sanctuary during combined antiretroviral therapy. AIDS Res Hum Retroviruses 34:123–125.
10.
Bozzi G, Simonetti FR, Watters SA, Anderson EM, Gouzoulis M, Kearney MF, Rote P, Lange C, Shao W, Gorelick R, Fullmer B, Kumar S, Wank S, Hewitt S, Kleiner DE, Hattori J, Bale MJ, Hill S, Bell J, Rehm C, Grossman Z, Yarchoan R, Uldrick T, Maldarelli F. 2019. No evidence of ongoing HIV replication or compartmentalization in tissues during combination antiretroviral therapy: implications for HIV eradication. Sci Adv 5:eaav2045.
11.
Fletcher CV, Staskus K, Wietgrefe SW, Rothenberger M, Reilly C, Chipman JG, Beilman GJ, Khoruts A, Thorkelson A, Schmidt TE, Anderson J, Perkey K, Stevenson M, Perelson AS, Douek DC, Haase AT, Schacker TW. 2014. Persistent HIV-1 replication is associated with lower antiretroviral drug concentrations in lymphatic tissues. Proc Natl Acad Sci USA 111:2307–2312.
12.
Chaillon A, Gianella S, Dellicour S, Rawlings SA, Schlub TE, De Oliveira MF, Ignacio C, Porrachia M, Vrancken B, Smith DM. 2020. HIV persists throughout deep tissues with repopulation from multiple anatomical sources. J Clin Invest 130:1699–1712.
13.
Devanathan AS, Fallon JK, White NR, Schauer AP, Van Horne B, Blake K, Sykes C, Kovarova M, Adamson L, Remling-Mulder L, Luciw P, Garcia JV, Akkina R, Pirone JR, Smith PC, Kashuba ADM. 2020. Antiretroviral penetration and drug transporter concentrations in the spleens of three preclinical animal models and humans. Antimicrob Agents Chemother 64:e01384-20.
14.
Thompson CG, Rosen EP, Prince HMA, White N, Sykes C, de la Cruz G, Mathews M, Deleage C, Estes JD, Charlins P, Mulder LR, Kovarova M, Adamson L, Arora S, Dellon ES, Peery AF, Shaheen NJ, Gay C, Muddiman DC, Akkina R, Garcia JV, Luciw P, Kashuba ADM. 2019. Heterogeneous antiretroviral drug distribution and HIV/SHIV detection in the gut of three species. Sci Transl Med 11:eaap8758.
15.
Srinivas N, Rosen EP, Gilliland WM, Kovarova M, Remling-Mulder L, De La Cruz G, White N, Adamson L, Schauer AP, Sykes C, Luciw P, Garcia JV, Akkina R, Kashuba ADM. 2019. Antiretroviral concentrations and surrogate measures of efficacy in the brain tissue and CSF of preclinical species. Xenobiotica 49:1192–1201.
16.
Scholz EMB, Mwangi JN, De la Cruz G, Nekorchuk M, Chan CN, Busman-Sahay K, Adamson L, Luciw P, Fedoriw Y, Estes JD, Rosen EP, Kashuba ADM. 2021. Quantitative imaging analysis of the spatial relationship between antiretrovirals, reverse transcriptase simian-human immunodeficiency virus RNA, and collagen in the mesenteric lymph nodes of nonhuman primates. Antimicrob Agents Chemother 65:e00019-21.
17.
Lamers SL, Rose R, Maidji E, Agsalda-Garcia M, Nolan DJ, Fogel GB, Salemi M, Garcia DL, Bracci P, Yong W, Commins D, Said J, Khanlou N, Hinkin CH, Sueiras MV, Mathisen G, Donovan S, Shiramizu B, Stoddart CA, McGrath MS, Singer EJ. 2016. HIV DNA is frequently present within pathologic tissues evaluated at autopsy from combined antiretroviral therapy-treated patients with undetectable viral loads. J Virol 90:8968–8983.
18.
Cesta MF. 2006. Normal structure, function, and histology of the spleen. Toxicol Pathol 34:455–465.
19.
Devanathan AS, White NR, Desyaterik Y, De la Cruz G, Nekorchuk M, Terry M, Busman-Sahay K, Adamson L, Luciw P, Fedoriw Y, Estes JD, Rosen EP, Kashuba ADM. 2021. The spatial relationship between antiretrovirals and cell-associated RT-SHIV RNA in the spleens of nonhuman primates using quantitative imaging analysis, abstr 7. Abstr 22nd Int Workshop Clin Pharmacol HIV Hepat Other Antivir Drugs.
20.
Deleage C, Wietgrefe SW, Del Prete G, Morcock DR, Hao XP, Piatak M, Bess J, Anderson JL, Perkey KE, Reilly C, McCune JM, Haase AT, Lifson JD, Schacker TW, Estes JD. 2016. Defining HIV and SIV reservoirs in lymphoid tissues. Pathog Immun 1:68–106.
21.
Haase AT, Henry K, Zupancic M, Sedgewick G, Faust RA, Melroe H, Cavert W, Gebhard K, Staskus K, Zhang ZQ, Dailey PJ, Balfour HH, Erice A, Perelson AS. 1996. Quantitative image analysis of HIV-1 infection in lymphoid tissue. Science 274:985–989.
22.
Haase AT. 1999. Population biology of HIV-1 infection: viral and CD4+ T cell demographics and dynamics in lymphatic tissues. Annu Rev Immunol 17:625–656.
23.
Estes JD, Kityo C, Ssali F, Swainson L, Makamdop KN, Del Prete GQ, Deeks SG, Luciw PA, Chipman JG, Beilman GJ, Hoskuldsson T, Khoruts A, Anderson J, Deleage C, Jasurda J, Schmidt TE, Hafertepe M, Callisto SP, Pearson H, Reimann T, Schuster J, Schoephoerster J, Southern P, Perkey K, Shang L, Wietgrefe SW, Fletcher CV, Lifson JD, Douek DC, McCune JM, Haase AT, Schacker TW. 2017. Defining total-body AIDS-virus burden with implications for curative strategies. Nat Med 23:1271–1276.
24.
North TW, Higgins J, Deere JD, Hayes TL, Villalobos A, Adamson L, Shacklett BL, Schinazi RF, Luciw PA. 2010. Viral sanctuaries during highly active antiretroviral therapy in a nonhuman primate model for AIDS. J Virol 84:2913–2922.
25.
Vibholm LK, Lorenzi JCC, Pai JA, Cohen YZ, Oliveira TY, Barton JP, Garcia Noceda M, Lu C-L, Ablanedo-Terrazas Y, Del Rio Estrada PM, Reyes-Teran G, Tolstrup M, Denton PW, Damsgaard T, Søgaard OS, Nussenzweig MC. 2019. Characterization of intact proviruses in blood and lymph node from HIV-infected individuals undergoing analytical treatment interruption. J Virol 93:e1920-18.
26.
Rothenberger MK, Keele BF, Wietgrefe SW, Fletcher CV, Beilman GJ, Chipman JG, Khoruts A, Estes JD, Anderson J, Callisto SP, Schmidt TE, Thorkelson A, Reilly C, Perkey K, Reimann TG, Utay NS, Nganou Makamdop K, Stevenson M, Douek DC, Haase AT, Schacker TW. 2015. Large number of rebounding/founder HIV variants emerge from multifocal infection in lymphatic tissues after treatment interruption. Proc Natl Acad Sci USA 112:E1126–E1134.
27.
Schacker TW, Nguyen PL, Beilman GJ, Wolinsky S, Larson M, Reilly C, Haase AT. 2002. Collagen deposition in HIV-1 infected lymphatic tissues and T cell homeostasis. J Clin Invest 110:1133–1139.
28.
Schacker TW, Brenchley JM, Beilman GJ, Reilly C, Pambuccian SE, Taylor J, Skarda D, Larson M, Douek DC, Haase AT. 2006. Lymphatic tissue fibrosis is associated with reduced numbers of naive CD4+ T cells in human immunodeficiency virus type 1 infection. Clin Vaccine Immunol 13:556–560.
29.
Estes JD, Wietgrefe S, Schacker T, Southern P, Beilman G, Reilly C, Milush JM, Lifson JD, Sodora DL, Carlis JV, Haase AT. 2007. Simian immunodeficiency virus-induced lymphatic tissue fibrosis is mediated by transforming growth factor beta 1-positive regulatory T cells and begins in early infection. J Infect Dis 195:551–561.
30.
Estes J, Baker JV, Brenchley JM, Khoruts A, Barthold JL, Bantle A, Reilly CS, Beilman GJ, George ME, Douek DC, Haase AT, Schacker TW. 2008. Collagen deposition limits immune reconstitution in the gut. J Infect Dis 198:456–464.
31.
Burgunder E, Fallon JK, White N, Schauer AP, Sykes C, Remling-Mulder L, Kovarova M, Adamson L, Luciw P, Garcia JV, Akkina R, Smith PC, Kashuba ADM. 2019. Antiretroviral drug concentrations in lymph nodes: a cross-species comparison of the effect of drug transporter expression, viral infection, and sex in humanized mice, nonhuman primates, and humans. J Pharmacol Exp Ther 370:360–368.
32.
Devanathan AS, Pirone JR, Akkina R, Remling-Mulder L, Luciw P, Adamson L, Garcia JV, Kovarova M, White NR, Schauer AP, Blake K, Sykes C, Burgunder EM, Srinivas N, Rosen EP, Kashuba ADM. 2020. Antiretroviral penetration across three preclinical animal models and humans in eight putative HIV viral reservoirs. Antimicrob Agents Chemother 64:e01639-19.
33.
Jochmans D, Anders M, Keuleers I, Smeulders L, Kräusslich H-G, Kraus G, Müller B. 2010. Selective killing of human immunodeficiency virus infected cells by non-nucleoside reverse transcriptase inhibitor-induced activation of HIV protease. Retrovirology 7:89.
34.
Trinité B, Zhang H, Levy DN. 2019. NNRTI-induced HIV-1 protease-mediated cytotoxicity induces rapid death of CD4 T cells during productive infection and latency reversal. Retrovirology 16:17.
35.
Chaurand P, Schriver KE, Caprioli RM. 2007. Instrument design and characterization for high resolution MALDI-MS imaging of tissue sections. J Mass Spectrom 42:476–489.
36.
Robichaud G, Barry JA, Garrard KP, Muddiman DC. 2013. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) imaging source coupled to a FT-ICR mass spectrometer. J Am Soc Mass Spectrom 24:92–100.
37.
Avalos CR, Price SL, Forsyth ER, Pin JN, Shirk EN, Bullock BT, Queen SE, Li M, Gellerup D, O’Connor SL, Zink MC, Mankowski JL, Gama L, Clements JE. 2016. Quantitation of productively infected monocytes and macrophages of simian immunodeficiency virus-infected macaques. J Virol 90:5643–5656.
38.
Pahar B, Kuebler D, Rasmussen T, Wang X, Srivastav SK, Das A, Veazey RS. 2019. Quantification of viral RNA and DNA positive cells in tissues from simian immunodeficiency virus/simian human immunodeficiency virus infected controller and progressor rhesus macaques. Front Microbiol 10:2933.
39.
Abreu CM, Veenhuis RT, Avalos CR, Graham S, Queen SE, Shirk EN, Bullock BT, Li M, Metcalf Pate KA, Beck SE, Mangus LM, Mankowski JL, Clements JE, Gama L. 2019. Infectious virus persists in CD4+ T cells and macrophages in antiretroviral therapy-suppressed simian immunodeficiency virus-infected macaques. J Virol 93:e00065-19.
40.
Hansen SG, Piatak M, Ventura AB, Hughes CM, Gilbride RM, Ford JC, Oswald K, Shoemaker R, Li Y, Lewis MS, Gilliam AN, Xu G, Whizin N, Burwitz BJ, Planer SL, Turner JM, Legasse AW, Axthelm MK, Nelson JA, Früh K, Sacha JB, Estes JD, Keele BF, Edlefsen PT, Lifson JD, Picker LJ. 2013. Immune clearance of highly pathogenic SIV infection. Nature 502:100–104.
41.
Hansen SG, Piatak M, Ventura AB, Hughes CM, Gilbride RM, Ford JC, Oswald K, Shoemaker R, Li Y, Lewis MS, Gilliam AN, Xu G, Whizin N, Burwitz BJ, Planer SL, Turner JM, Legasse AW, Axthelm MK, Nelson JA, Früh K, Sacha JB, Estes JD, Keele BF, Edlefsen PT, Lifson JD, Picker LJ. 2017. Addendum: immune clearance of highly pathogenic SIV infection. Nature 547:123–124.
42.
Bokhart MT, Rosen E, Thompson C, Sykes C, Kashuba ADM, Muddiman DC. 2015. Quantitative mass spectrometry imaging of emtricitabine in cervical tissue model using infrared matrix-assisted laser desorption electrospray ionization. Anal Bioanal Chem 407:2073–2084.
43.
Barry JA, Groseclose MR, Castellino S. 2019. Quantification and assessment of detection capability in imaging mass spectrometry using a revised mimetic tissue model. Bioanalysis 11:1099–1116.
44.
Dorr P, Westby M, Dobbs S, Griffin P, Irvine B, Macartney M, Mori J, Rickett G, Smith-Burchnell C, Napier C, Webster R, Armour D, Price D, Stammen B, Wood A, Perros M. 2005. Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunodeficiency virus type 1 activity. Antimicrob Agents Chemother 49:4721–4732.
45.
Temesgen Z, Siraj DS. 2008. Raltegravir: first in class HIV integrase inhibitor. Ther Clin Risk Manag 4:493–500.
46.
Gilead Sciences, Inc. 2018. Emtriva package insert. Gilead Sciences, Inc., Foster City, CA.
47.
European Medicines Agency. 2004. REYATAZ (atazanavir) new drug application, scientific discussion. European Medicines Agency, London, United Kingdom.
48.
Gilead Sciences, Inc. 2019. Viread package insert. Gilead Sciences, Inc., Foster City, CA.
49.
Thompson CG, Bokhart MT, Sykes C, Adamson L, Fedoriw Y, Luciw PA, Muddiman DC, Kashuba ADM, Rosen EP. 2015. Mass spectrometry imaging reveals heterogeneous efavirenz distribution within putative HIV reservoirs. Antimicrob Agents Chemother 59:2944–2948.
50.
Bokhart MT, Nazari M, Garrard KP, Muddiman DC. 2018. MSiReader v1.0: evolving open-source mass spectrometry imaging software for targeted and untargeted analyses. J Am Soc Mass Spectrom 29:8–16.
Information & Contributors
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Published In
Antimicrobial Agents and Chemotherapy
Volume 66 • Number 8 • 16 August 2022
eLocator: e00609-22
PubMed: 35856680
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© 2022 American Society for Microbiology. All Rights Reserved.
History
Received: 29 April 2022
Returned for modification: 26 May 2022
Accepted: 7 July 2022
Published online: 20 July 2022
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