Consistent Behavior of Immune Myeloid Cells Across Ages in Humans and Monkeys, Mainly Controlled by Blood Factors During Bacterial Infection

All procedures were performed under sterile conditions. One day prior to the experiment, 50 μL of the E. coli stock suspension was inoculated into 10 mL of LB medium and grown overnight until reaching an optical density 0.6 at 600 nm (in average 1.29 × 10¹⁰ bacteria per mL). The next day, 500 μL of E. coli culture was centrifuged for 2 min at 2350 g and resuspended in 250 μL of phosphate-buffered saline (PBS). Subsequently, 250 μL of fixative (10% neutral buffered formalin, Element Company, Saint-Petersburg, Russia) was added and thoroughly mixed by pipetting and vortexing. The samples were incubated for 30 min at room temperature using rotary mixer at 10 rpm. The bacteria were then washed 3 times with 500 μL of PBS via centrifugation for 2 min at 2350 g each time. After the final wash, the pellet was resuspended in 500 μL PBS.

For staining with the LIVE/DEAD Vio dye (LIVE/DEAD Fixable Violet Dead Cell Stain Kit, Thermo Fisher Scientific, Waltham, MA, USA, cat. no. L34955), the dye stock solution was prepared by diluting one vial of dye in 50 μL of DMSO. Subsequently, 1 μL of dye was used for staining of 500 μL of bacterial stock suspension. After incubation at room temperature in the dark for 1 h, labeled bacteria were washed three times with 500 μL of PBS using centrifugation for 2 min at 2350 g. Finally, the pellet was reconstituted in 200 μL of PBS. The labeled bacterial cells were counted and stored at 4 °C for up 24 h prior to use.

Additionally, for simultaneous confocal microscopy experiments, bacteria were conjugated with FITC and biotin as previously described [44]. The efficiency of conjugation was assessed by measuring fluorescence signal intensity of L/D Vio or FITC using BD LSRFortessa flow cytometerv (BD Biosciences, Franklin Lakes, NJ, USA), using 405 nm laser with a 450/50 nm filter for L/D Vio; 488 nm laser with a 530/30 nm filter for FITC; 561 nm laser with a 582/15 nm filter for AF568 and were compared with the control fixed, unconjugated bacteria. Next, L/D Vio (or FITC) labeled E. coli were conjugated with biotin using a FluoReporter Mini-biotin-XX Protein Labeling Kit (Thermo Fisher Scientific, MA, USA; cat. no. F6347) in accordance with the manufacturer's instructions. Sterile glycerol was added to the labeled bacterial suspension to a concentration of 10%, and the E. coli concentration was adjusted to 10¹⁰ cells/mL with PBS/10% glycerol solution. The suspension was aliquoted into 20 µL stock, frozen, and stored at −20 °C for 2–3 months until use.

To evaluate biotinylation efficiency, 1 μL (1 µg) of streptavidin-AF568 (Thermo Fisher Scientific, MA, USA, cat.no. S11226) was added to 300 μL of a double-labeled bacterial suspension (10⁷ E. coli/mL). After incubation at +4 °C for 30 min, the bacteria were washed twice with PBS by centrifugation for 2 min at 2350 g, resuspended in 200 μL of PBS, and analyzed on a BD LSRFortessa to assess the percentage of bacteria positive for both L/D Vio and AF568 fluorescence.

Healthy donors from different age groups were selected for the study after providing voluntary informed consent. Qualified personnel at the medical center of Sirius University collected venous blood samples from the median cubital vein. The study was approved by the Ethics Committee of Sirius University of Science and Technology (protocol dated 6 March 2023). The list of the donors and the additional information about donors' health are attached in(see). Supplementary data Supplementary Tables S1 and S2

Volunteer donors participating in the study were divided into two age groups: 14 donors (7 women) aged 22–33 years (junior) and 11 donors (6 women) aged 61–73 years (senior).

Qualified veterinarians from the National Research Center "Kurchatov Institute" Medical Primatology Complex selected a group of sexually mature, healthy cynomolgus monkeys (Macaca fascicularis), also known as crab-eating or long-tailed macaques. The macaques were divided into two age groups: 14 animals (5 females) aged 4–7 years (junior), corresponding to approximately 15–23 human years, and 7 animals (3 females) aged 19–27 years (senior), corresponding to approximately 62–89 human biological age.

All monkeys were originally imported from Southeast Asia and bred for a long time (several generations), at a breeding facility in Sochi.

Animals were housed individually or in family groups under ambient environmental conditions (temperature 21–28 °C, relative humidity 40–70%) with exposure to natural daylight. Sanitary measures were conducted in accordance with applicable local regulations. Animals had ad libitum access to fresh drinking water supplied via a centralized water system in compliance with state sanitary standards (SanPiN 2.1.3684-21, SanPiN 1.2.3685-21 [45,46]; Rospotrebnadzor, Moscow, Russia; https://www.rospotrebnadzor.ru/files/news/SP2.1.3684-21_territorii.pdf↗, assessed on 22 December 2025). Water quality was regularly monitored, and samples were periodically analyzed for potential contamination.

The animals received a nutritionally balanced diet containing adequate amounts of proteins, fats, carbohydrates, vitamins, and dietary fiber. The diet consisted of complete feed, rice porridge with raisins, vegetables, fruits, briquetted feed, and biscuits, provided in accordance with average daily feed intake standards. Feeding was performed three times daily: from 8 a.m. to 9 a.m. animals received granulated balanced feed; from 11 a.m. to 12 p.m. juicy feed (fruits and vegetables), cookies, and rice porridge; and from 2 p.m. to 3 p.m. granulated balanced feed, in accordance with local regulations (SOP AC 11 "Feeding Individually Caged Monkeys").

All experimental procedures were conducted in accordance with the guidelines of the International Association for Evaluation and Accreditation of the Care of Laboratory Animals. All macaques included in the study were clinically healthy, as confirmed by routine veterinary examinations, negative test results for tuberculosis and herpes virus B, and a review of previous medical records.

After anesthesia, a qualified veterinarian collected blood from the superficial femoral vein and bone marrow from the trabecular zone of the tibia using a Jamshidi needle. The work was carried out in accordance with the approved ethical permission of the Kurchatov Complex of Medical Primatology (No. 02-3pr dated 21 March 2024).

All blood samples were collected in tubes with heparin to prevent coagulation (BD, Vacutainer), then the number of leukocytes, granulocytes, monocytes and lymphocytes was measured in whole blood using a MEK-7300K hemoanalyzer (Nihon Kohden, Tokyo, Japan). For erythrocyte lysis, 10 volumes of BD Pharm Lyse™ lysing buffer were added to the cell pellet and incubated for 10 min at room temperature. After incubation, the cells were resuspended in BD Biosciences Cell Wash containing antibodies.

Following the "3R" principles (Replacement, Reduction, and Refinement) that guide ethical and humane animal research, aiming to minimize the use and suffering of animals, some studies were not performed on experimental animals due to the lack of age differences in human myeloid cell populations.

After erythrocyte lysis, blood cells were resuspended in RPMI medium with 10% FBS and additional glutamine (final concentration—2 mM, all from Capricorn Scientific, Ebsdorfergrund, Germany) using centrifugation for 5 min at 330 g. Cells were placed in a CO₂ incubator for 1–2 h (5% CO₂, 37 °C), then counted, and cell integrity was controlled on an Evos M3000 fluorescence microscope (Thermo Fisher). Cells were resuspended in 250 μL of medium with bafilomycin A (final concentration 100 nM), incubated for 1 h (5% CO₂, 37 °C). After completion of incubation, SPiDER reagent (Dojindo Molecular Technologies, Rockville, MD, USA; SG0403), 1 μM final, was added to the leukocytes for vital staining for SA-β-Gal activity and incubated for 1 h (5% CO₂, 37 °C), according to the manufacturer's instructions. Finally, the cells were washed by centrifugation and resuspended in a buffer suitable for further procedures.

The cells were washed by centrifugation once for 5 min at 330 g and resuspended in RPMI/10% FBS/Glutamine. An aliquot of the cells was stained 1:1 with acridine orange (final concentration 10 μg/mL) and the concentration of live cells was counted using Countess 3 (Thermo Fisher Scientific, MA, USA). Leukocytes at a concentration of 500,000 cells in 100 μL (100 μL/sample) were placed in a polypropylene non-adherent tube (Eppendorf, Hamburg, Germany), the temperature was stabilized in a CO₂ incubator (37 °C, 5% CO₂) for 5 min and the E. coli preparation was added (leukocytes: E. coli ratio 1:20). The E. coli suspension was prepared as described in Section 2.1. The suspension was incubated for 1 h at 37 °C, 5% CO₂ and then placed on ice to stop phagocytosis. Next, 10 volumes of BD CellWash Buffer were added to the cells, centrifuged at 330 g for 5 min (10 °C), and the supernatant containing unabsorbed bacteria was removed. Staining with antibodies and streptavidin-DL550 (DyLight 550) or streptavidin-AF568 was performed to identify subpopulations and bacteria remaining outside on the cell surface.

To validate the developed protocols, flow cytometry and confocal microscopy were simultaneously performed as described in our previous work [44]. Figure 1A presents a diagram illustrating the possible distribution of bacteria inside and on the surface of phagocytes based on their presumable fluorescence; Figure 1B shows a representative FACS plot depicting the fluorescence of blood cells following phagocytosis and streptavidin staining. Bacteria that were phagocytosed during a one-hour incubation fluoresced only in the FITC channel, indicating their internalization. In contrast, bacteria attached to the cell surface but not engulfed remained accessible for additional staining with streptavidin–DL550, allowing identification of their surface localization.

For comparison of the two methods, after phagocytosis and staining, double-positive cells (containing bacteria on the outside) were sorted using a BD FACSAria III. These sorted cells were mounted on glass slides for confocal microscopy analysis. Representative cells were imaged layer by layer (Z-stack with 1 µm intervals) at different positions along the Z-axis (Figure 1C). In this example, bacteria located on the cell surface appear in the upper layers and are double positive for FITC and streptavidin–DL550, whereas bacteria inside the cells are stained only with FITC and show no streptavidin–DL550 staining. Multiple engulfed bacteria within granulocytes are further illustrated in Supplementary Movie S1.

After discarding the supernatant containing unabsorbed bacteria, the blood cell suspension was for 30 min stained with set of antibodies (Supplementary Tables S3 and S4) on ice, and then the cells were washed once by adding BD CellWash to 1.5 mL and centrifuging for 5 min at 330 g.

Samples were analyzed using BD LSRFortessa flow cytometer. The gating strategy involved sequential identification of all leukocytes based on forward scatter (FSC) and side scatter (SSC) parameters, followed by cell duplets exclusion. Subpopulations of phagocytic leukocytes were then identified according to fluorescence intensity signals as negative for CD3, CD19/20 and positive for CD14 or CD16 (). These subpopulations were analyzed for phagocytosis by fluorescence of L/D Vio and streptavidin-AF568. The complete gating strategies are presented in. Supplementary Tables S3 and S4 Supplementary Figures S1 and S2

To perform confocal microscopy, myeloid blood cells before and after completion of phagocytosis, were sorted using BD FACSAria III for side scatter (SSC) and CD45-APC-eFluor780, into CD45⁺ population of phagocytes, as well as into a single positive and double positive population having E. coli inside or both outside and inside the phagocytes. Then the cells were used for additional antibody staining or directly for slides mounting for confocal microscopy.

3 mL of whole blood, after erythrocyte lysis, was resuspended in BD CellWash buffer. The cells were washed, and live, singlet cells were sorted based on side scatter (SSC) into monocyte and granulocyte populations using a BD FACSAria III flow sorter. After sorting, the cell populations were resuspended in PBS and then transferred to fixation and permeabilization buffers for intracellular staining, following the manufacturer's protocol for the BD Cytofix/Cytoperm™ Fixation/Permeabilization Kit.

Briefly, the cells were washed with PBS by centrifugation (5 min at 330 g), and the cell pellet was immediately resuspended in 100 μL Cytofix/Cytoperm (BD, #51-2090KZ) and incubated for 20 min at 4 °C. The cells were centrifuged, the supernatant was discarded, and the pellet was resuspended and washed twice with 200 μL ready-to-use solution Perm/Wash buffer (BD, #51-2091KZ). Finally, the cells were resuspended in 50 μL Perm/Wash buffer containing either anti-γH2AX antibodies (polyclonal, Cusabio, Wuhan, China; cat. #CSB-PA105411, 1:100) or anti-HMGB1 antibodies (clone EPR3507, Abcam, Waltham, MA, USA; cat. #ab79823, 1:100). The cells were incubated at 4 °C for 16 h in the dark. After primary antibody incubation, the cells were washed twice with 200 μL ready-to-use solution BD Perm/Wash buffer for 5 min each. The cells were then resuspended in 100 μL Perm/Wash buffer containing goat anti-rabbit AF555 secondary antibody (polyclonal, Abcam, cat. #ab150086, 1:500) and incubated at room temperature for 1 h. The cells were washed 5 min twice with 200 μL Perm/Wash buffer. Next, the cells were stained with DAPI (1 μg/mL) for 5 min in the dark at room temperature, followed by two washes with 200 μL PBS for 5 min each. After staining, the cells were concentrated by centrifugation for 5 min at 330 g, resuspended in 10 μL PBS, and placed onto Polysine Adhesion Slides (Thermo Fisher Scientific, MA, USA). The slides were incubated in a humid chamber for 15 min at room temperature and then mounted in Fluoroshield medium (Abcam, cat. #ab104135) according to the manufacturer's instructions. Finally, the samples were covered with coverslips and sealed with transparent nail polish.

Confocal fluorescence images were obtained using an inverted point scanning confocal microscope (LSM 980 based on Axio Observer 7, Carl Zeiss, Oberkochen, Germany) through an oil immersion 63× objective (Plan-Apochromat, NA 1.4, Carl Zeiss, Germany). We used simultaneous acquisition setup to obtain 3D images of macrophages and bacteria (Figure 1C) FITC, DL550 and CD45-APC-eFluor780 channels were acquired by using 488 nm, 543 nm and 639 nm lasers simultaneously; AOTF transmission was set to 0.0008%, 0.4% and 1.1%, respectively. Three channels of spectral detector were set as follows: 498–532 nm for FITC, 562–629 for DL550 and 649–750 for CD45-APC-eFluor780. Detector gains were adjusted to fill image dynamic range. Confocal scan zoom was set from 3× to 7× depending on cell size. The image parameters were set as follows: pixel dwell time 1.49 µs; pixel size dx = dy = 0.08 µm, z-stack step dz = 0.4 µm. To obtain images of Alexa Fluor 555 channel (HMGB1 and γH2AX, with good signal-to-noise ratio, Alexa Fluor 555 and DAPI channels were acquired sequentially by using 543 nm and 405 nm lasers, respectively; AOTF transmission was set to 0.1% and 0.2%, respectively. Spectral detector band was set to 545–641 for Alexa Fluor 555 and to 399–484 for DAPI. Detector gains were adjusted to fill image dynamic range on brightest samples. Confocal scan zoom was set from 7× to 9× depending on cell size. The image parameters were set as follows: pixel dwell time 4.95 µs; pixel size dx = dy = 0.07 µm, z-stack step dz = 0.19 µm. All images were obtained using Zen software (version Zen Blue 3.2, Carl Zeiss, Germany).

The results of HMGB1 and γH2AX staining were processed using Anaconda Navigator and Jupiter Notebook in Python v.3.10, using built-in libraries. The processing principle is presented below.

To visualize subcellular (throughout entire the cell volume) the distribution of fluorophores, three-dimensional confocal image stacks were converted into two-dimensional intensity maps. This was achieved by summing voxel intensities along the optical (z) axis for each fluorescence channel, producing a single projection image per cell. This approach effectively transforms volumetric data into a representative 2D image, analogous to the median fluorescence intensity (MFI) metric used in flow cytometry, capturing the total fluorophore content on a pixel-by-pixel basis

The resulting projected images were then linearly rescaled to utilize the full 8-bit range, enhancing contrast and compensating for variability in absolute signal between cells. An automatic global threshold was determined using Otsu's method to distinguish cellular regions from background noise. The largest contiguous object identified in the binary mask was designated as the cell of interest.

To differentiate nuclear versus cytoplasmic compartments, a secondary channel mask was applied (Figure 2). Specifically, subtracting the inverse binary mask isolated cytoplasmic pixels, while subtracting its complement isolated nuclear pixels. For each compartment, pixel intensities were compiled into histograms representing the full fluorescence distribution across all pixels within that region. The half-width at half-maximum (HWHM) of each histogram served as a quantitative measure of distribution breadth, reflecting fluorophore dispersion within the compartment.

Within each segmented region, two key metrics were calculated: N: The total number of non-zero pixels (i.e., pixels with detectable fluorescence). S: The sum of all pixel intensities (integrated fluorescence signal). Dividing S by N yielded the mean pixel intensity, serving as an image-based analog to flow cytometry's MFI.

To compare distribution widths between nuclear and cytoplasmic compartments (and across experimental conditions), HWHM values were statistically analyzed using a one-way ANOVA, followed by Tukey's HSD test (significance threshold p < 0.05). This statistical framework allowed for rigorous assessment of differences in fluorophore dispersion within cellular compartments, extending the MFI concept to spatially resolved fluorescence imaging.

Finally, pixel value histograms for each compartment were constructed asH(k) = #{(y,x)|I_sub(y,x) = k},k ∈ [1, 255] where I_sub(y, x) is the intensity of the subcellular region at coordinate (y, x).

The data processing program is available at the following reference link: https://github.com/ElijahBiocinth/MFI-h2ax↗ (accessed on 31 October 2025).

Whole blood was drawn into vacuum tubes containing clot activator and serum separation gel (1 mL). The samples were allowed to clot, centrifuged to separate serum, which was then aliquoted, frozen, and stored at −80 °C for subsequent cytokine analysis.

For assessment of myeloid cell cytokine secretion after stimulation the whole blood samples were aliquoted into 48-well culture plates (NEST, Wuxi, China) at 250 µL per well. The plates were incubated at 37 °C with 5% CO₂ for 2 h to allow cell adherence. After incubation, non-adherent cells were discarded and then washed twice with phosphate-buffered saline (PBS). Adherent cells were then cultured in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS), glutamine, and HEPES buffer. Cells were stimulated with either purified Escherichia coli lipopolysaccharide (LPS; final concentration 100 ng/mL, Sigma-Aldrich, St. Louis, MO, USA) or with fixed E. coli bacteria at a leukocyte-to-bacteria ratio of 1:20. Supernatants were collected 22 h post-stimulation, centrifuged to remove cell debris, and stored at −80 °C until use. Cytokine levels (TNF and IL-6) in the supernatants were analyzed using enzyme-linked immunosorbent assay (ELISA) kits (Vector-Best, Koltsovo, Novosibirsk region, Russia), following the manufacturer's instructions. Supernatants from unstimulated cultures served as negative controls.

The day before the experiment, to obtain an overnight bacterial culture, 50 μL of E. coli stock (strain DH5α) in 10% glycerol was placed in 10 mL of LB medium and incubated at 37 °C with shaking at 200 rpm in Excella E25R shaker-incubator (New Brunswick Scientific, Edison, NJ, USA). The next day, the optical density of the bacterial suspension was measured at 600 nm using a Multiskan SkyHigh microplate spectrophotometer (Thermo Fisher Scientific, MA, USA), as described above. The overnight culture was divided into two parts: one part was fixed as described above, and the other part was kept alive. The concentration of fixed bacteria was determined using a Novocyte 3000 flow cytometer (Agilent, Santa Clara, CA, USA), based on forward and side scatter indices. The unfixed (alive) portion of the overnight E. coli culture was serially diluted for further experiments.

To obtain plasma, 2 mL of whole heparinized blood from each donor was centrifuged for 7 min at 600 g. The hematocrit of each donor was measured using a hematology analyzer MEK-7300K (Nihon Kohden, Tokyo, Japan) to determine the plasma volume equivalent to 100 μL of whole blood. The plasma volume equivalent to 100 μL of blood for each donor was calculated using the formula: plasma volume = 100 μL of whole blood—hematocrit. To determine the titer of the initial stock, bacteria were plated on LB-agar plates using limiting dilution concentration. The number of bacterial colony-forming units (CFU-B) was counted after 24 h. This titer value was taken as the control concentration of bacteria at zero-time point (C₀). Bacteria were then incubated in 100 μL of whole blood and an equivalent volume of plasma in Eppendorf tubes on a rotary shaker (60 g) at 37 °C. The first group of whole blood and plasma samples was pre-incubated with 10⁸ fixed bacteria for 4 h, after which live bacteria (10⁷ per sample) were added and incubated for an additional 4 or 24 h. In the second group, live bacteria (10⁷ per sample) were added immediately to the sample and incubated for 4 h.

To assess the effect of plasma on bacterial viability in the absence of antibacterial and humoral factors, plasma was heat-inactivated at 56 °C for 30 min, immediately cooled, and then incubated with live bacteria for 4 h. After incubation, all samples were plated on LB agar Petri dishes and incubated overnight at 37 °C. Colony counting was performed after 24 h.

To evaluate the neutralizing capacity of the samples, for each sample, the CFU-B values after incubation of plasma with bacteria were normalized to the control CFU-B at the zero-time point (C₀), calculated at the beginning of the experiment before incubation with blood components.

Flow cytometry data were analyzed with BD FlowJo v. 10.10. Confocal microscopy images were processed with ImageJ/Fiji v. 1.54f.

Primary flow cytometry data, phagocytic assay results, were documented, processed, and composed in Microsoft office 2019. Data visualization and statistical analyses were performed using GraphPad Prism v.9.5.0, utilizing its built-in statistical tools. Statistical significance is indicated in the graphs as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****), and ns (not significant). During study, researchers were "blinded" to the age group and sex of the samples during data acquisition and analysis.

Aging, including aging of the immune system, is a non-linear process [47]. Systemic changes in multiple molecular components associated with aging occur nonlinearly, with increases observed between the ages of 40 and 60 years [48]. Additionally, the average age at diagnosis of an autoimmune disease is 54 years [49]. In our study, we demonstrated the stability of innate immune system cells with increasing age between younger (22–33 years) and older (57–73 years) groups. As we interested in physiological aging, the study only included healthy working adults and did not encompass individuals with compromised immune function, the very elderly (over 80 years), or middle-aged individuals (35 to 55 years).

In contrast to studies based on clinical cohorts, the primary aim of the present study was to characterize universal indicators of innate immune system aging typical of an average, clinically healthy individual regardless of sex, rather than to identify individuals with accelerated aging or early-onset pathologies within the population. Furthermore, one of the primary objectives of this study was to compare aging-associated evolutionally conserved traits between macaques and humans.

Although the study included a cohort of elderly macaques, many of which were of near-terminal age, the sample did not display pronounced signs of innate immune system aging. It should be noted that animals surviving to such advanced ages represented a group of clinically healthy dominant males and females who may have benefited from superior access to resources due to their privileged social status within the group. In this context, the study can be viewed as an investigation of longevity in non-human primates, potentially associated with a robust innate immune system. Additionally, due to restricted access to the samples and labor-intensive experiment designs, we examined, in general, 25 humans (11 senior) and 21 monkeys (7 senior) which is a sample size slightly exceeding the minimum required for statistical analysis. Thus, the study is limited to identifying moderate changes that may occur during the aging process.

In addition, we assessed the stability of innate immune cells by analyzing selected markers of senescent cells. These markers include γH2AX, HMGB1, and SA-β-Gal. These markers may indicate cellular stress, genetic instability, and impaired lysosomal function. We also compared the phenotype, secretion profile (TNF and IL-6), serum and blood antimicrobial properties and functional properties of cells from groups of young and elderly donors. However, there are other markers of aging, such as ROS, telomere shortening, epigenetic drift, and mitochondrial dysfunction. These markers have not been studied and may be of interest for future research. However, cellular functions remained stable across age groups, suggesting that the aforementioned molecular processes were not disrupted in this case.

In conclusion, we found a predominance of humoral factors over cellular factors in antimicrobial defense (against E. coli, strain DH5α). The uniqueness of our approach is in the simultaneous assessment of both cellular and humoral blood functions, which enables a better understanding of how the immune system combats various bacteria—specifically, whether they are sensitive or insensitive to complement. Thus, the developed protocols can be used for any strain to comprehensively understand the role of cellular and humoral factors in various infectious diseases and infectious immunity in general.

To determine age-related differences in immune cell content in blood and bone marrow samples from humans and macaques in two age groups (junior and senior, see Section 2), the percentages of major cell populations were analyzed using flow cytometry. However, no statistically significant differences in the composition of the main blood cell populations were found between age groups of donors (Figure 3A). Only a tendency toward a decrease in the proportion of granulocytes in the blood was observed in elderly individuals of both species. However, in the bone marrow of macaques, the granulocyte percentage, conversely, tended to increase with age. To study interspecies differences, three groups of samples were compared: human blood and the blood and bone marrow of cynomolgus macaques (Figure 3B). The number of immune cells in human and macaque blood samples did not differ statistically, while statistically significantly fewer T- and B-lymphocytes were found in the bone marrow than in the blood samples, and more granulocytes were found in the bone marrow. A trend toward a decrease in the number of non-classical CD16⁺CD14^lo monocytes (non-classical monocytes, ncMOs) and CD14⁺CD16⁺ monocytes (intermediate monocytes, inMOs) was also detected in macaque bone marrow.

Thus, the results indicate that peripheral blood and bone marrow differ in the relative proportions of lymphocytes and granulocytes, with the latter showing an age-associated tendency to accumulate in the bone marrow. However, in the study population, no significant differences in the proportion of myeloid populations were observed between elderly and young individuals in either humans or monkeys.

As described previously, cellular aging is accompanied by an increase in genetic instability [50]. In this study, we developed a method for quantifying nuclear and cytoplasmic fluorescence intensities by applying dimensionality reduction to a numerical parameter used in flow cytometry—mean fluorescence intensity (MFI)—to analyze staining intensity at the single-cell level. This approach enabled quantifying the staining of myeloid blood cells with antibodies targeting HMGB1 and γH2AX, allowing separate assessment of nuclear and cytoplasmic staining for each marker (Figure 4). Specifically, for HMGB1, we evaluated its content in the nucleus and cytoplasm, while for γH2AX, we assessed its nuclear content relative to cytoplasmic background. Although γH2AX can also be detected in the cytoplasm during early apoptosis [51], apoptotic cells were not observed in our samples.

Cells from donors in two age groups were stained to compare markers of genetic instability associated with cellular aging. We hypothesized that senescent myeloid cells from elderly donors would exhibit differences in these markers. The analysis included cells from both junior and senior donors. For each donor and marker, thirty granulocytes and monocytes sorted from blood were analyzed. Images were processed separately to evaluate the distribution of HMGB1 and γH2AX staining within the nucleus and cytoplasm. Nuclear areas were defined based on DAPI staining (Figure 4).

Comparison between the two healthy donor age groups revealed no statistically significant differences in fluorescence intensity or in the localization (nuclear versus cytoplasmic) of HMGB1 and γH2AX staining (Figure 4B).

Another important marker of cellular aging is the increased activity of the senescence-associated β-galactosidase (SA-β-Gal) enzyme. Recently, a highly efficient protocol for detecting SA-β-Gal activity has been developed. This method utilizes the SPiDER dye, which is effectively immobilized within cells, allowing for accumulation of a fluorescent signal and increased sensitivity to β-galactosidase activity. While the optimal enzymatic activity of general β-galactosidase in young cells occurs at pH 4, the activity of SA-β-Gal in senescent cells peaks around pH 6. To facilitate this, the pH was shifted to the optimal range for SA-β-Gal activity using the pH modulator Bafilomycin A1 (Baf A1), thereby preferentially enhancing SA-β-Gal activity within the cells. Under these conditions, increased SA-β-Gal activity can be observed in aged cells [52,53]. Additionally, both we and other researchers have demonstrated that this dye can be used in conjunction with antibodies in multicolor flow cytometry, enabling the isolation of cell populations with high SA-β-Gal activity [8,54,55].

A study of SA-β-Gal activity in human blood cell samples from two age groups, as well as in monkey blood and bone marrow cells, showed no increase in SA-β-Gal activity with age within myeloid populations and B cells in either species (Figure 5A). Moreover, in bone marrow samples from elderly monkeys, a trend toward decreased SA-β-Gal activity was observed.

However, significant differences were detected between cell populations regardless of age. For example, granulocytes and monocytes—particularly the CD14⁺CD16^lo monocyte subset (classical monocytes, cMOs)—exhibited high SA-β-Gal activity in blood cells (Figure 5B). Moreover, SA-β-Gal activity was significantly higher across all myeloid populations compared to lymphoid populations (B cells) in both species. This finding aligns with data from rodents, where both endogenous β-Gal and SA-β-Gal activity were markedly elevated in myeloid cells [55].

A comparative analysis of SA-β-Gal activity in immune cell populations from the blood and bone marrow of macaques showed that SA-β-Gal activity is significantly higher in the blood—particularly in the major myeloid populations, including granulocytes and cMOs—compared to the bone marrow (Figure 5C). Since total β-Gal level measured here reflects increased lysosomal activity (lysosomal mass) and perhaps of carbohydrate metabolism within lysosomes, it can be inferred that these myeloid populations become activated upon entering the peripheral circulation. This is especially evident in cMOs, whereas ncMOs exhibit increased SA-β-Gal activity specifically in the bone marrow. These differences may indicate distinct functional roles for these populations in peripheral blood versus bone marrow.

To investigate age-related changes in the functional activity of myeloid cells, we examined one of the key aspects of innate immunity—the phagocytic activity of monocytes and granulocytes. Cell phagocytic activity was assessed in whole blood or in RBC-lysed blood without prior adhesion. Phenotypic analysis of granulocytes—the main cell type involved in phagocytosis—revealed significant differences in phenotype between humans and cynomolgus monkeys. Human granulocytes lack surface CD14, a component of the CD14/TLR4/MD2 receptor complex that recognizes LPS. In contrast, macaque blood contains an average of 80.4 ± 24.1% CD14⁺ granulocytes, and macaque bone marrow contains 96.9 ± 13.3% CD14⁺ granulocytes. Additionally, macaque granulocytes were negative for the CD16 (FcγRIII) marker, whereas in humans, approximately 92.4 ± 13.5% of granulocytes were CD16⁺ (Figure 6A,B). Despite these phenotypic alterations, no significant differences were observed in the phagocytic functional properties of macaque and human granulocytes (Figure 6C,D).

The in-house developed fixed bacterial prep carried two labels to distinguish between engulfed bacteria and those unengulfed but trapped on the surface. This enabled the determination of the spatial localization of bacteria, distinguishing those inside (in) and outside (out) the phagocytes (see Section 2). In this context, age differences in phagocytic activity were also investigated. As a result of the analysis, no age differences in phagocytosis were found in myeloid populations over a wide range of ages in human and macaque blood samples, and macaque bone marrow (Figure 6C). Granulocytes are the most active bacterial scavengers, given their prominent role in phagocytosis and their abundance in blood and bone marrow, thereby executing the majority of phagocytic activity in these compartments. However, when comparing monocytic subsets across all samples, it was observed that, in both humans and macaques, the cMOs tend to retain larger quantities of unengulfed bacteria on their surface during phagocytosis, compared to the ncMOs and granulocytes (Figure 6D) after a 1 h incubation. No other populations exhibited such a significant bias toward bacterial surface capture and retention. Moreover, this phenomenon was consistent across both species, suggesting that it is an evolutionarily conserved process.

Analysis of serum levels of the proinflammatory factors TNF and IL-6 showed no significant age-related differences in either humans or monkeys. Only a trend toward decreased IL-6 levels was observed in humans (Figure 6E). The response to stimulation with either LPS or whole bacteria also did not change significantly between the two age groups (Figure 6F).

To clarify the fate of unengulfed bacteria remaining on the surface of cMOs and granulocytes (GRs), we extended the incubation time of monocytes in suspension to 1, 2, and 4 h to assess the phagocytosis time course (Figure 7A). Results showed that the percentage of cMOs containing only internalized bacteria increased significantly by 4 h of incubation. Concurrently, there was a visible trend toward a decrease in the number of cMOs retaining bacteria on their surface. Since excess bacteria were added (leukocyte:bacteria ratio of 1:20), we hypothesize that cMOs can bind more bacteria than they can phagocytize, which may prevent bacterial dissemination throughout the body. This suggests that classical monocytes may undergo multiple rounds of phagocytosis lasting more than 4 h. In contrast, granulocytes showed an opposite trend: a decrease in cells containing only internalized bacteria and an increase in cells with bacteria attached to their surface.

Our data above showed that human granulocytes are negative for the CD14 marker. However, with prolonged incubation with bacteria, the MFI for CD14 significantly increases on granulocytes (by 4 h of incubation), although the overall CD14 level remains low compared to monocytes. Moreover, the median CD14 level in cMO decreases over 4 h, which is consistent with literature data on CD14 internalization into cells upon LPS stimulation (Figure 7B) [56].

To compare the contributions of myeloid cells and plasma humoral factors to bacterial infection control across donors of different age groups, we developed an assay to assess bacterial viability after exposure to whole blood or plasma (Figure 8A). In all assays, we observed a significant reduction in colony-forming units of live bacteria (CFU-B) incubated with whole blood or plasma compared to the control (an aliquot of bacteria not exposed to blood components). However, we did not find statistically significant differences in CFU-B counts after exposure to whole blood or plasma from donors in the two age groups (Supplementary Figure S3).

The comparative analysis of incubation with whole blood and plasma allowed us to evaluate the combined contribution of myeloid cell functions (degranulation, NETosis, phagocytosis) and plasma humoral factors alone. The results were assessed by measuring the reduction in viable bacterial colonies (CFU-B) after 4 h of incubation. The experiments employed limiting dilution, with live bacteria added at varying concentrations to generate results suitable for visual counting.

Results were compared against a control containing an average of 10⁸ CFU-B of E. coli. Variants 1 and 3 represent the results of bacteria exposed to whole blood, while variants 2 and 4 represent exposure to blood plasma (Figure 8A). In variants 1 and 2, cells were pre-incubated with paraformaldehyde-fixed bacteria for 4 h to deplete serum humoral factors and pre-activate myeloid cells. The control consisted of CFU-B after 4 h of bacterial incubation in LB medium. Several experiments demonstrated that preincubation of blood plasma at 56 °C for 30 min, followed by a 4 h incubation with bacteria, did not significantly alter the number of CFU-B compared to incubation in LB medium alone, which is optimal for supporting E. coli growth. This indicates that native humoral protein factors are essential for bacterial neutralization, and thermal inactivation effectively abolishes the bactericidal activity of blood plasma. Figure 8B presents the statistical analysis of the data from these experimental variants. Incubation with whole blood reduced the number of viable bacteria by approximately 6 orders of magnitude after 4 h, while incubation with plasma reduced viable bacteria by about 6–7 orders of magnitude on average. This suggests that the volume of plasma, equivalent to 100 µL of blood used, can kill approximately 10⁷ E. coli bacteria, highlighting the high bactericidal capacity of plasma alone.

Statistical analysis revealed significant differences between plasma samples pre-incubated with killed bacteria (variant 2) and those without such pre-incubation (variant 4). This indicates that plasma factors, such as components of the complement system and antibodies, interact with fixed bacteria, thereby depleting the plasma's bactericidal potential (Figure 8B).

No statistically significant differences were observed when comparing the contributions of plasma alone versus whole blood (naturally containing plasma and cellular components) to bacterial killing.

Conversely, a tendency toward reduced neutralizing capacity was noted in whole blood samples (variant 3) compared to plasma samples (variant 4). Therefore, the data do not establish a significant role for the functional activity of innate immune cells in the neutralization of the Gram-negative E. coli strain DH5α. Nonetheless, the protocol we developed can be adapted to investigate the contributions of cellular blood components in the clearance of other pathogens.