Mitochondrial profiling reveals dynamic, sex- and age-specific mitochondrial phenotypes in human immune cell subtypes

How mitochondria functionally differ between immune cell subtypes, between the sexes, and with aging has not been defined in humans. We deploy a high-throughput mitochondrial phenotyping platform to define cell-type specific mitochondrial features in human circulating immune cell subtypes. In women and men spanning four decades of life, we find that mitochondrial content, mitochondrial DNA copy number (mtDNAcn), and respiratory chain enzymatic activities vary by up to 3.5-fold between neutrophils, monocytes, B and T lymphocyte subtypes. Mitochondria exhibit specific age- and sex-related differences among individual cell subtypes, including an age-related increase in mtDNAcn. In an intensive repeated-measures study, we also identify remarkable weekly variation in mitochondrial content and respiratory chain function, which is partially correlated with changes in circulating biomarkers. Our results also define multivariate mitochondrial phenotypes − mitotypes − that distinguish lymphoid from myeloid cell types, naive-to-memory lymphocyte states, and moderately differ between women and men. Finally, a comparison of mitochondrial features in purified cell subtypes and in peripheral blood mononuclear cells (PBMCs) invites caution in using cell type mixtures to infer person-level mitochondrial function. Together, these findings identify dynamic cell-type specific variation in mitochondrial biology among circulating leukocytes and provide foundational knowledge to develop interpretable blood-based assays of mitochondrial health.


Introduction
Mitochondria are unique organelles that contain their own genome and functionality based on the cell type they inhabit ( In relation to aging, increasing age is associated with continuous stimulation of innate and adaptive immune cells, leading to immune dysregulation and increased risk of chronic disease (Meier et al., 2020). In relation to cell type distribution, aging is also associated with decreasing abundance of circulating naïve T cells, particularly CD8 + T cells, and increasing abundance of We then repeated this analysis with CBC-derived cell proportions and found that compared to women, men had on average 28% more monocytes (Supplemental Figure 3), consistent with our FACS results. Conversely, compared to men, women had on average 20% more platelets.
Platelet abundance also tended to decrease with age, a point discussed later.
Next, we built on these results to define mitochondrial phenotypes for different cell subtypes. Our analysis focused on two broad aspects of mitochondrial biology: i) mitochondrial content, measured by citrate synthase (CS) activity, a Kreb's cycle enzyme used as a marker of mitochondrial volume density (Larsen et al., 2012), and mtDNAcn, reflecting the number of mtDNA copies per cell; and ii) RC function measured by complex I (CI), complex II (CII) and complex IV (CIV) enzymatic activities, which reflect the capacity for electron transport and respiratory capacity and serve here as a proxy for maximal oxidative phosphorylation (OXPHOS) capacity ( Figure 1a). To obtain sufficient numbers of cells for mitochondrial phenotyping, we selected the 6 most abundant cell subtypes for each individual and isolated 5 million cells for each sample. Because memory subtypes were relatively rare, central and effector memory (CM and EM) subtypes were pooled for CD4 + and/or CD8 + (CM-EM). This generated a total of 340 biological samples, including 136 biological replicates, yielding 204 individual person-to-cell-type combinations used for mitochondrial phenotyping. 7 content, B cells had the highest mtDNAcn with an average 451 copies per cell compared to neutrophils and NK cells, which contained only an average of 128 (g=5.94, p<0.0001) and 205 copies (g=3.84, p<0.0001) per cell, respectively (Figure 2b). Naïve and memory CD4 + and CD8 + T lymphocytes had intermediate mtDNAcn levels of ~300 copies per cell, with the exception of CD8 + naïve cells (average of 427 copies per cell). Between cell types, we note that CS activity and mtDNAcn differed by up to 3.52-fold, providing initial evidence of strong effect size segregation of cell type-specific mitochondrial phenotypes among both innate and adaptive cell lineages.
In relation to RC function, monocytes had the highest Complexes I, II, and IV activities. To understand the proportion of shared variance between mitochondrial content features (CS and mtDNA) and between RC complex enzymatic activities, we quantified their intercorrelations among all cell types. Interestingly, CS and mtDNAcn were only weakly correlated with each other, and in some cases were negatively correlated. For RC complexes CI, CII, and CIV, which physically interact and whose function is synergistic within the inner mitochondrial membrane, correlations tended to be positive, as expected ( Figure 2f). However, some of these correlations were weak, and some were either not correlated or even negatively correlated within specific cell types. These relatively weak and absent inter-correlations between mitochondrial features in some cell types reveal that rather than being redundant measures, each metric provides relatively independent information about immune cell mitochondrial biology.

Mitochondrial health index (MHI) among cell subtypes
To further define how RC function relates to mitochondrial content, we integrated content and function features into a single metric known as the mitochondrial health index (MHI) (Picard et al., 2018). By adding the three mean-centered features of RC function together as a numerator (CI+CII+CIV), and dividing this by the combination of content features (CS+mtDNAcn), we obtained an index reflecting RC capacity on a per-mitochondrion basis (Figure 3a). Of all cell types, monocytes had the highest MHI, which was 72% higher than B cells (g=3.78, p<0.0001) which presented the lowest MHI (Figure 3b). On average, memory T cell subtypes exhibited a 6-18% higher MHI relative to their naïve precursors (CD4 + : g=0.40, p=0.32; CD8 + : g=1.02, p=0.019),

Mitochondrial features exhibit differential co-regulation across immune cell subtypes
We next asked to what extent mitochondrial markers are correlated among different cell subtypes in the same person. For example, does the individual with the highest mtDNAcn in B cells also have the highest mtDNAcn in other cell types? Could having high mtDNAcn or low CIV activity constitute coherent properties of an individual that are expressed ubiquitously across cell types, or are these private properties to each cell subtype?
To assess the extent to which mitochondrial features are correlated across cell subtypes (co-regulation) we used Spearman correlation matrices, which revealed a general positive association (Figure 3c). Within a person, CS activity was moderately co-regulated across cell subtypes (average correlation rz'=0.63), albeit with some exceptions (i.e., cell pairs that were less correlated than average, such as CD4 + CM-EM T cells & B cells). Similarly, mtDNAcn was generally positively correlated between cell types (average correlation rz'=0.53). We note that compared to CM-EM subtypes, naïve T lymphocyte mtDNAcn values tended to be more strongly correlated with other cell subtypes (r=0.32-0.93), indicating that co-regulation of mtDNAcn varies by cell type. In comparison, RC enzymes showed markedly lower inter-correlations across cell types ( Figure 3d) and some cell types showed no correlation with other cell types, revealing a substantially lower degree of co-regulation among RC components than in mitochondrial content features. However, there was moderate evidence of co-regulation across cell types for MHI (average rz'=0.37).
These findings show that while immune cells exhibit markedly different mitochondrial content and RC activities (see Figure 2), there is evidence for a person-level factor. On the other hand, the absence of correlation among several cell types, particularly for RC enzymatic activities, suggests that the biology of immune cell subtypes could be relatively independently determined by cell-autonomous factors (e.g., stimulation of specific cell subtypes or subpopulations). This provides a rationale for performing cell-type specific studies when examining the influence of external exposures and person-level factors on immune cells' mitochondrial bioenergetics, including the influence of sex and age.

Mitochondrial content and RC function differ between women and men
In each cell subtype, we then systematically compared CS activity, mtDNAcn, RC activity, and MHI features between women and men (Figure 4a-f). Across all cell subtypes examined, 9 compared to men, women had higher CS activity (range: 4-29%, g=0.20-1.52, Figure 4a) and higher CII activity (range:1-10%, g=0.03-0.56, Figure 4d). The consistency of this finding across all cell types suggests that these differences are stable, sexually dimorphic characteristics.
Women also showed 29% higher CS activity in CD8 + memory subsets and 26% higher CI activity in monocytes, differences characterized by strong effect sizes (g=1.52 and 1.35, respectively).

Age influences mitochondrial content and RC function
We then quantified the association between the same mitochondrial features and age (correlations) for each cell subtype. With increasing age, CS activity was relatively unaffected except in neutrophils, where it decreased ~7% per decade (r=-0.63, p=0.031) (Figure 4g). In comparison, mtDNAcn increased with increasing age among all cell subtypes except CD8 + naïve T cells which was, interestingly, the cell subtype exhibiting the strongest age-related decline in abundance in circulation. CD4 + naïve T cells and monocytes showed the largest age-related change in mtDNAcn, marked by a ~10% increase in the number of mitochondrial genomes per cell per decade of life (r=0.54 for both, p=0.022 and 0.023 respectively, Figure 4h). Thus, in purified cell subtype preparations (free of platelets and other confounds), aging is associated with a consistent increase in leukocyte mtDNAcn.
For RC function, we mostly observed an equal number of cell subtypes with either positive or negative correlations with age, with the exception of CII (Figures 4i-k). CII activity was positively correlated with age across all cell types, except for monocytes and NK cells where it was independent from age. In contrast, CI and CIV activities were only weakly associated with age, highlighting again differential regulation of RC components. Of all cell types, CD8 + CM-EM T cells showed the most consistent positive associations for all RC activities and age, notably for CII where the enzymatic activity per cell increased ~21% per decade (r=0.85, p=0.0004). CD4 + naïve T cells were the only cell type showing a consistent, albeit modest, increase of both mitochondrial content and function with age (range:1-10% increase per decade, r=0.08-0.54, Supplemental 10 Figure 4). Similarly, MHI correlated positively with age in CD8 + CM-EM T cells, indicating that energy production capacity on a per-mitochondrion basis tends to increase with age in this cell type (r=0.56, p=0.052), possibly related to the acquisition or maintenance of memory characteristics. In contrast, MHI tended to correlate negatively with age in monocytes (r=-0.35, p=0.15) ( Figure 4l). Overall, these data demonstrate that age-related changes in CS activity, mtDNAcn, and RC function are largely cell-type specific.

Cell subtype distributions exhibit natural week-to-week variation
Whether and how much mitochondrial content or function change over time in various cells and tissues is not well defined, and to our knowledge has not been examined in circulating human immune cells. Here we repeated the same blood collection, cell sorting, and downstream mitochondrial enzymatic and molecular procedures described above to perform weekly mitochondrial profiling on a healthy 34-year-old Caucasian man (author M.P.) over 9 weeks ( Figure 5a). To preclude external influences on cell subtype distributions and mitochondrial function, each blood draw occurred at the same time of day (9am), on the same day of the week, after a standardized breakfast, and comparable physical activity. The 6 most abundant cell subtypes for this individual included neutrophils, NK cells, monocytes, naïve and CM-EM subtypes of CD4 + T cells, and naïve CD8 + T cells. The resulting samples were processed on the mitochondrial phenotyping platform in parallel with the cohort samples, ensuring that results across the cohort and repeat participant are directly comparable.
We first quantified the circulating proportions (% of total leukocytes by FACS) of 12 cell types to establish how much the abundance of each cell subtype naturally varies within a person (Supplemental Figure 5). Cell type distribution is known to exhibit natural diurnal variation week variation in abundance was CD8 + EM (root mean square of successive differences (rMSSD)=0.22, coefficient of variation (C.V.)=19.5%), which varied between 6.3% (week 2, highest) to 3.3% of total cells (week 9, lowest). Thus, even the least variable cell type can decrease in proportion by approximately half in the span of a few weeks. In contrast, other subtypes such as CD4 + TEMRA (min=0.02% to max=0.62%) and neutrophils (min=3.9% to max=31.8%) varied week-to-week by as much as an order of magnitude (i.e., 10-fold), similar to between-person differences observed in the cohort (see Figure 1e and Supplemental Table 2).
Antibody-producing B cells, which had the highest mtDNAcn, varied by up to 1.1-fold (min=0.86% to max=1.8%). Together, these time-course results illustrate the dynamic remodeling of circulating 11 leukocyte populations within a single person. In addition, these data suggest that cell mixtures (e.g. PBMCs) are likely to be confounded by unequal abundance of different cell types on different occasions of sampling, a point addressed later.

Mitochondrial content, mtDNAcn and RC activity exhibit natural week-to-week variation
We quantified mitochondrial features reflecting content and RC function in each available cell type across the 9 time points. The first observation was that the robust cell type differences reported above in our cohort were conserved in the repeat participant. Monocytes had the highest RC activity, neutrophils the lowest, and CD4 + and CD8 + T lymphocytes showed intermediate activities. Similarly, the cell subtype with the highest mtDNAcn was CD8 + naïve T cells (average across 9 weeks=400 copies/cell, 427 in the cohort) and neutrophils exhibited the lowest mtDNAcn in both the repeat participant and the cohort (average=123 copies/cell, 128 in the cohort).
Within specific cell subtypes, we found that all mitochondrial metrics exhibited substantial variation over time. Within the same person, different cell types showed week-to-week variation ranging from 7.3 to 21.9% for CS activity, 4.1 to 18.3% for mtDNAcn, 8.4 to 37.5% for CI, 4.1 to 15.3% for CII, and 15.2 to 64.4% for CIV (Figure 5b-f). Similarly, MHI in each subtype varied by 8.3 to 20.6%, with the naïve subsets of CD4 + and CD8 + T lymphocytes exhibiting the largest weekto-week changes (rMSSD=0.28 and 0.22, respectively) ( Figure 5g). In most cases, the observed variation was significantly greater than the established technical variation in our assays (see Supplemental Table 3), providing confidence that these changes in mitochondrial content and function over time reflect robust biological changes.
To put this natural biological variation in perspective, we directly compared the inter-and intra-individual variation of mitochondrial metrics. Specifically, we asked: relative to differences observed among a heterogeneous cohort of women and men spanning 4 decades of life, how much do the same metrics naturally vary within a single person. Remarkably, for several mitochondrial metrics, the 9-week range and natural variation within the same person was similar to the between-person differences among our 21-participant cohort. Figure 5h provides a sideby-side comparison of the cohort and repeat participant CS activity on the same scale. The C.V. of CS in monocytes was 17.0% for the cohort and 20.2% in the repeat participant. Similarly, in CD4 + naïve T cells, the dynamic range for CS activity among both the cohort and repeat participant was similar. Supplemental Figure 6 shows side-by-side comparisons of between-and within-person variation for mtDNAcn, CI, CII, CIV, and MHI. The similar degree of variation in the cohort and repeat participant indicates that dynamic variation is a generalizable property across 12 all mitochondrial features examined. This also suggests that future research designs should incorporate repeated measures of specific mitochondrial metrics to ensure the reliability of mitochondrial content and function features.
Next, we assessed the co-regulation of mitochondrial content and RC activity across cell subtypes within the same person over time. Despite the small sample of timepoints for these comparisons, similar to our cohort findings, CS and mtDNAcn were most highly correlated across cell types (average rz'=0.55-0.70) (Figure 5i-j). Again, RC activities were only moderately correlated, indicating partial co-regulation of mitochondrial energetics across some cell subtypes.
Of the RC complexes, CI was the most consistent across cell subtypes (average rz'=0.39), whereas CIV showed the least co-regulation (average rz'=0.06) including several negative correlations ( Figure 5j). For example, when CIV activity for this individual increased in monocytes, CIV activity tended to similarly increase in CD4 + CM-EM T cells (r=0.71, p=0.088), but tended to decrease in CD8 + naïve T cells (r=-0.68, p=0.11). Altogether, these within-person data were consistent with our cohort findings (between individuals) indicating partial co-regulation of mitochondrial features across cell subtypes, including some negative associations that remain unexplained.
Compiling all cell subtype pairs for each mitochondrial feature, we then systematically evaluated the level of agreement between the cohort and the repeat participant. In other words, we asked if two cell subtypes (e.g., CD4 + and CD8 + naïve) are highly correlated for a given mitochondrial feature (e.g., CS) among the cohort, do these cell subtypes also show high correlation within a single person over time? The presence of most datapoints in the upper right quadrants of Figure 5k supports the general conclusion that mitochondrial features -particularly CS and mtDNAcn -are generally co-regulated across cell types within both the cohort and a single person over time. However, the level of agreement between the cohort and repeat participant was generally weak to moderate (r=0.08-0.35, absolute values), possibly pointing to person-level differences in how mitochondrial behavior among different cell types relate to each other.
Because these and previous results somewhat differed by mitochondrial feature, and because each feature reflects a slightly different aspect of mitochondrial biology, we then mathematically combined multiple variables to investigate cell subtype-specific mitochondrial phenotypes.
Mitotypes differ between immune cell subtypes . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 13 As in other complex biological systems, the function and behavior of mitochondria require synergy among multiple features. For that reason, the functional characteristics of mitochondria may be defined by multivariate approaches examining the inter-relations of multiple mitochondrial features. This logic is similar to body mass index (BMI), which puts height and weight into a BMI ratio that is more easily interpretable and meaningful than either height or weight features alone.
For example, in human mitochondrial tissues, the ratio of CIV and CII activities (also known as COX/SDH ratio) is used in diagnoses of mitochondrial dysfunction, whereas alone either CIV or CII activity is less easily interpretable (Picard et al., 2012b). Thus, integrating primary features of mitochondrial content, genome abundance, and RC function produces integrated mitochondrial phenotypes, or mitotypes, which reflect the functional specialization of mitochondria among different cell subtypes, or among dynamic cellular states.
Similar to BMI, the mitotypes analyzed (listed and defined in Supplemental Figure 7) are computed from the ratio of two or more mitochondrial features. Each mitotype can be visualized as a scatterplot with two variables of interest as the x and y axes (Figure 6a). In the example in Figure 6a, the hypothetical cell type A has a higher value for feature 2 relative to feature 1 (Mitotype A), while cell type B has a higher value for feature 1 relative to feature 2 (Mitotype B).
Note that if both features increase or decrease following the same proportions, the ratio is the same and follows a diagonal on the mitotype graph, reflecting an invariant mitotype. Thus, similar to BMI, changes in mitotypes are reflected by perpendicular movement relative to the diagonal in the mitotype space.
Given that leukocyte subtypes differ widely in their bioenergetic properties, we asked in our cohort (n=21 individuals) if different cell subtypes have different mitotypes. The first mitotype examined puts in relation mtDNA copies per cell (mtDNAcn) relative to mitochondrial content per cell (CS activity), creating a mitotype (mtDNAcn/CS) that reflects mtDNA density per mitochondrion ( Figure 6b). Alone, this mtDNA density mitotype provided remarkable separation of cell subtypes. Neutrophils and NK cells were low on both metrics, B cells were high on both metrics, monocytes had the lowest mtDNA density, whereas CD8 + naïve T cells exhibited the highest mtDNA density of all cell subtypes tested. Figures 6c-e illustrate other mitotypes including i) CII activity per unit of mitochondrial genome (CII/mtDNAcn), as well as more complex combinations of variables such as ii) CI activity per unit of mtDNA (CI/mtDNAcn ratio on y axis) in relation to mtDNA density (mtDNAcn/CS activity on x axis), and iii) CI activity per mitochondrial content (CI/CS, y) in relation to mtDNA density (mtDNAcn/CS, x). We understand these mitotype combinations to reflect the well-known principles of mitochondrial functional specialization (Calvo 14 et al., 2016; Picard et al., 2012a) and to represent individual mitochondrial "personality types'' tailored to subserve specific bioenergetic and biosynthetic requirements within immune cell subtypes.
This mitotype-based analysis of mitochondrial characteristics revealed two main points.
First, cells of the innate and adaptive immune subdivisions contain mitochondria that differ not only quantitatively in their individual metrics of mitochondrial content and RC activity, but that exhibit marked qualitative differences. This is illustrated by the distinct clustering of neutrophils, monocytes and NK cells (innate) within similar mitotype spaces, and the clustering of all lymphocyte subtypes together in different spaces. Compared to cells of the innate immune compartment, lymphocytes (adaptive) had higher mtDNAcn and lower respiratory chain activity.
A second insight from this analysis is that compared to naïve subsets of CD4 + and CD8 + T cells, which themselves have relatively distinct mitotypes (e.g., CII/mtDNAcn, Figure 6c), both memory CD4 + and CD8 + subtypes converged to similar mitotype spaces. Functionally, this naïve-tomemory mitotype transition is well known to involve a metabolic shift including changes in spare respiratory capacity, mitochondrial content, glucose and fatty acid uptake (Nicoli et al., 2018; van der Windt et al., 2012). The mitotype analysis showed that compared to naïve cell subtypes, memory subtypes exhibit 26-29% lower mtDNA density per mitochondrion, but an 8-23% increase in RC activity per mitochondrion in CD4 + T cells, although not in CD8 + T cells (Figure 6f).

Stability of mitotypes
Next, we examined how stable these cell-type specific mitotypes were in the independent repeat participant dataset. We plotted matching cell types for the cohort and for the repeat participant on the same mitotype plots. As for the cohort, cell types belonging to the innate and adaptive immune divisions similarly clustered together, and naïve and memory subtypes differences were also validated at the within-person level (Figure 6g-i), demonstrating the conserved nature of immune cell mitotypes.
To examine the biological stability of mitotypes across individuals, we determined the effect sizes between cell subtypes for selected mitotypes in our cohort, and then compared these to the magnitude of the differences between the cohort and the repeat participant for specific cell subtypes ( Figure 6j). First, consistent with the results for individual mitochondrial features (see Figure 2), the effect sizes comparing mitotypes between cell subtypes ranged from moderate to very large. For example, mitotype differences between monocytes and CD8 + naïve T lymphocytes had effect sizes ranging from g=2.5 to 3.5. Neutrophils and NK cells, which were among the most 15 similar cell types on most mitotype plots, had Hedges' g values ranging from 0.2 (small) to 1.2 (very large), reflecting some considerable mitotype differences even among these cell types. In comparison, inter-individual variation (cohort n=21 vs repeat n=9) across different mitotypes was over an order of magnitude smaller, with pooled effects sizes being on average 12.5-fold smaller than cell type differences. Overall, these data across both the mixed cohort of women and men, and within-person repeated-measures, reveal that immune cell subtypes have distinct and robust mitotypes consistent with their divergent bioenergetic requirements.

Evidence for a sex and age bias in mitotypes
We next sought to systematically examine if mitotypes differ between women and men.
Mitotypes were organized into five categories of indices based upon their features, yielding a total of 16 mathematically-distinct mitotypes (see Supplemental Figure 7). For each mitotype, we quantified the magnitude of the difference between women and men by the effect size (g), ranked all mitotype x cell subtype combinations (16 mitotypes x 9 cell subtypes), and analyzed the distribution of these indices by sex. The majority of mitotypes reflecting mitochondrial RC activities per CS activity were higher in men (p<0.0001, Chi-square), while RC activity per mtDNA density (p<0.001) and RC activity per genome in relation to mtDNA density mitotypes (p<0.01) were predominantly higher in women (Figure 7a). The magnitude of sex differences ranged from 17% higher in men (CI/CS in CD4 + CM-EM T cells, g=1.14) to 38% higher in women (CII/mtDNA density in neutrophils, g=1.37) (Figure 7b). The direction of sex differences for all mitotypes (e.g. higher in women or in men) with effect sizes is illustrated in Figure 7c. Compared to purified cell subtypes, the magnitude of sex differences in the PBMCs cell mixture appeared blunted. The average effect size across all mitotypes was 0.31 (small) in CD4 + naïve T cells, compared to monocytes where the average effect size was 0.71 (medium).
Using the same approach, we then systematically quantified the relationship between mitotypes and age. Mitotypes reflecting RC activity per CS activity were predominantly positively correlated with age (p=0.046), while RC activities per genome in relation to mtDNA density mitotypes were generally negatively correlated with age (p=0.012) (Figure 7d). This finding is consistent with the overall age-related increase in mtDNAcn across cell subtypes, and could indicate a general decrease in the RC output per unit of mitochondrial genome with aging in immune cells. The strength of these correlations ranged from r=-0.67 to 0.75 (Figure 7e). The correlations of individual mitotypes with age for each cell subtype are shown in Figure 7f.
Overall, these findings reveal consistent patterns of differences in immune cell mitotypes between women and men, and to a lesser extent with aging. Importantly, these data also indicate 16 that the observed differences vary considerably by cell subtype. Whereas innate immune cells exhibited the strongest sex differences, B cells exhibited the largest age-related mitotype changes.

Associations of blood biomarkers with subtype-specific mitochondrial features
To explore the source of inter-individual differences and within-person dynamics over time, we asked to what extent blood biomarkers were correlated with subtype-specific mitochondrial features. To do so, we examined sex hormones, inflammatory markers, metabolic markers, and standard clinical blood biochemistry in relation to subtype-specific mitochondrial features ( Figure   8a). Because mitochondrial features in specific cell types, as well as blood biomarkers, were both associated with sex and age, we examined sex-and age-adjusted partial correlations. At the cohort level, associations between blood biomarkers and cell subtype mitochondrial phenotypes were relatively weak ( Figure 8b). These results indicate that circulating neuroendocrine, metabolic and inflammatory factors are unlikely to explain a large fraction of the variance in inter-individual differences in mitochondrial biology, pointing instead to behavioral, genetic and other influences as the source of inter-individual variation.
We then examined this question at the within-person level where week-to-week variation is independent of constitutional and genetic influences, and where behavior (e.g., levels of physical activity, sleep patterns, etc.) is more stable relative to the behavioral differences between women and men of different ages. Compared to the cohort, the strength of associations between biomarkers and mitochondrial metrics across cell types was on average 1.7-fold larger in the repeat participant (average absolute values, rz'=0.39 for the repeat participant vs 0.23 for the cohort) ( Figure 8c). Another notable finding was the difference in the correlation patterns between different cell subtypes, which again suggest that different mitotypes, or the cells that host them, may exhibit different sensitivities to circulating neuroendocrine, metabolic, or biochemical factors.
Notable patterns of associations between circulating biomarkers and mitochondrial features emerged at the within-person level. For instance, lipid levels including triglycerides, total cholesterol, and low-and high-density lipoproteins (LDL, HDL) were positively correlated with CS activity and mtDNAcn, with the largest effect sizes observed among innate immune cells: neutrophils, NK cells, and monocytes ( Figure 8c, red area on the heatmap). In these cells, lipid levels accounted on average for 53% of the variance (r 2 ) in CS activity and 47% in mtDNAcn. We note that some of these estimates are likely overestimated due to the small number of repeated measures. Nevertheless, compared to CS and mtDNAcn, these associations were less consistent 17 for RC enzyme activities and MHI. We speculate that the positive association of lipid levels and We next sought to examine potential divergences in the associations among the cohort compared to the repeat participant. It is established that different individuals show remarkably different baseline levels of both mitochondrial features and blood biochemistry measures (e.g., some people have higher blood cholesterol than others). Moreover, our data shows that within a person, there is also extensive physiological variation in mitochondrial features and in blood biochemistry over time. Therefore, we reasoned that even if each participant in our cohort was to exhibit a strong positive correlation between LDL and CS activity over time, sampling each person on a single randomly selected occasion would not reveal this positive correlation at the group level. As an illustration of this point, in the repeat participant we found an association between lipids and mitochondrial content (LDL and neutrophil CS activity, r=0.90, p<0.01) that was not observed in the cohort, where each datapoint represents a single time point for a given person

Association of cell subtypes abundance and enzymatic activities with total PBMCs
Compared to isolating purified cell subtypes, cell mixtures such as PBMCs are more easily (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint differences between cell subtypes across all mitochondrial features, it is reasonable to assume that cell mixtures are likely to be confounded by cell type distribution. Therefore, we analyzed the same mitochondrial features in both FACS-purified cell subtypes and PBMCs of the same person, and first asked to what extent PBMCs reflect mitochondrial content and function of isolated cell subtypes. If PBMCs were comprised solely of those individual cell types, mitochondrial activities normalized on a per-cell basis in PBMCs could only be as high as the highest cell subtype it contains (e.g., B cells), and as low as the lowest (e.g., neutrophils). Curiously, we found that PBMCs had up to 2.9-fold higher levels of CS, CI, and CII activity per cell than any of the individual cell subtypes measured (see Figure 2), casting initial doubt on the validity of mitochondrial activity measures in PBMCs. This discrepancy could be attributable to contamination of PBMCs with platelets, which contain mitochondria but no nucleus used for normalization per cell (see section below). PBMCs also had an unexpectedly high MHI, similar to NK cells and monocytes, and generally higher than naïve lymphocytes ( Figure   3b). We also asked to what extent the influence of sex and age on mitochondrial phenotypes observed in purified cell subtypes can be detected in total PBMCs. Our data revealed a markedly smaller association between sex and PBMCs mitochondrial activities (g=0.10-0.40) relative to isolated cell subtypes (Figure 4a-f). Likewise, PBMCs mitochondrial content and function features did not significantly correlate with age (r=0.008-0.15, absolute values) (Figure 4g-l). These results are in stark contrast to individual cell subtypes, revealing that PBMCs either do not show ageand sex-related associations that exist among some of its constituent cells, or that these associations are strongly blunted.
When evaluating the stability of PBMCs mitochondrial content and function over time in the repeat participant, we found that PBMCs CS activity, mtDNAcn, and RC activity varied by 9.8-61.9%, similar to the variation observed in isolated subtypes (C.V. range: 4.1-64.4%) (Figure 5ag). For mtDNAcn, CI, and CII activities, PBMCs showed either similar or higher week-to-week variation than neutrophils, the cell subtype with the greatest dynamic variation on these . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. We then compared PBMCs and other cell subtypes based on the same multivariate mitotype spaces described above. As Figure 6b-e shows, PBMCs generally exhibit a similar mitotype (diagonal line from the origin of the plot) as innate immune cell subtypes (monocytes, NK cells, and neutrophils), appearing relatively distinct from lymphocyte subpopulations. In relation to mitotype sensitivities to sex and age differences, similar to analyses on single metrics, PBMCs showed among the weakest associations with either variable (Figure 7c and f). Thus, even if specific cell subtypes reveal sex-and age-related differences, PBMCs provide modest between-person sensitivity to detect these associations.

PBMCs are a biologically distinct entity from individual cell subtypes
To understand the origin of the divergences identified above between PBMCs and individual cell subtypes, we determined how much potential influence the abundance of various circulating immune cell subtypes has on individual mitochondrial metrics in PBMCs. The null hypothesis here is that cell type abundances (e.g., having more or fewer B cells in circulation) does not influence PBMCs mitochondrial features. We first estimated the proportion of variance in PBMCs mitochondrial features attributable to cell type abundance measured by flow cytometry  Table 2). The proportion of other cell types accounted for more modest portions (<14%) of the variance in PBMCs, although we note that higher abundance of memory cells tended to be negatively associated with PBMCs activities.
Interestingly, the pattern of associations was markedly different when we analyzed within-  Figure 10a). Therefore, in order to partly resolve the origin of the discrepancies between isolated cell subtypes and PBMCs notes above, we sought to quantify the contribution of platelets to total mitochondrial content and activity features in PBMCs.
We first asked if the abundance of platelets from the CBC data in the cohort changes with age. In our cohort, we found that platelet count decreased by 6% for each decade of life ( contrast, as noted above (see Figure 2), our analysis in purified cell subtypes reveal an agerelated increase in mtDNAcn among most cell types. Therefore, platelet contamination could influence PBMCs mitochondrial features and explain the discrepancies with purified cell types, including why PBMCs have higher CS activity values than any of its constituent cells.
To directly test this hypothesis, we ran a separate experiment to assess the influence of platelets on total PBMCs mitochondrial features ( Figure 10e). This consisted of isolating PBMCs using standard methods, which included two standard low speed "platelet depletion" steps (see Methods for details). A portion of the obtained PBMCs platelets were then actively immunodepleted using anti-CD61-tagged magnetic beads. Three fractions were ultimately processed for mitochondrial phenotyping and compared: i) total PBMCs, ii) platelet-depleted PBMCs, and iii) platelet-enriched eluate from the depletion procedure. As expected, platelet depletion decreased mtDNAcn, CS, and RC activities, indicating that contaminating platelets . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 22 exaggerated specific mitochondrial features by 9-22%, with exception to CIV ( Figure 10f).
Moreover, the platelet-enriched eluate showed 23-100% higher mitochondrial activities relative to total PBMCs, providing further evidence that platelet depletion was effective and that platelets inflated estimates of mitochondrial abundance and RC activity in standard PBMCs.
Interestingly, the composite MHI was not affected by the platelet depletion procedure, suggesting that this multivariate index of respiratory chain capacity on a per-mitochondrion basis may be more robust to platelet contamination than individual features. Overall, these data demonstrate platelet-contamination even in carefully prepared PBMCs, and show that the magnitude of platelet contamination represents a potential source of noise for PBMCs-based mitochondrial studies.

Discussion
Mapping the naturally occurring bioenergetic differences among various tissues and cell types is critical to further our understanding of the role of mitochondria in human health and disease. Here we have used the immune system as a model to examine this question, isolating and phenotyping multiple immune cell subtypes among a diverse cohort of women and men, and in repeated weekly measures in the same participant. Using a high-throughput mitochondrial phenotyping platform including five primary mitochondrial features and a derived MHI, we find large functional differences between immune cell subtypes in mitochondrial features reflecting both mitochondrial content and respiratory chain function. These mitochondrial features also vary moderately by age and sex. In addition, we uncover large week-to-week within-person variation in both cell subtype proportions and mitochondrial behavior. We also introduce multivariate mitotypes, which constitute a first step towards identifying stable cell-type specific bioenergetic profiles. Together with the direct comparison of these features in PBMCs cell mixtures, these data in specific leukocyte populations provide foundational knowledge on mitochondrial phenotypes in human immune cells, their sensitivity to inter-individual variables such as age and sex, and In relation to metabolic profiling, our ability to deploy mitochondrial phenotyping to hundreds of samples with a throughput and sensitivity not previously possible revealed consistent cell type differences in multiple mitochondrial features, reflecting mitochondrial content and respiratory chain function. By any standard, the magnitude of the observed differences, for example in mtDNAcn (up to 3.5-fold differences between cell subtypes, g=5.94), is very large. The combination of a heterogenous cohort and a repeat participant design also allowed us to further quantify the stability of these cell subtype differences over time.
Examining the molecular basis for these cell type differences could represent a unique opportunity to delineate the mechanisms responsible for controlling mitochondrial mass, mtDNAcn, and/or functional specialization within accessible human cells. In relation to mitochondrial genome regulation, the age-related increase in mtDNAcn among most cell types deserves particular attention.
Defining the temporal aspects of human immunometabolism is a major frontier that, when overcome, will transform our knowledge of fundamental immune and mitochondrial biology, and allow for the rational design of therapeutic approaches (Artyomov and Van den Bossche, 2020).
A primary finding of this work is the natural within-person variation of mitochondrial features, providing insight into the temporal dynamics of immunometabolism in specific human cell subtype populations. The existence of large inter-individual differences in mitochondrial features between the immunologically-defined cell subpopulations noted above naturally called for repeatedmeasures conducted with the same degree of biological specificity. As such, cell sorting removes the potential confound of week-to-week changes in cell type distributions, an inherent confounding variable in PBMCs, and therefore adds confidence in the robustness of such temporal mitochondrial variation. This within-person analysis revealed that mitochondrial features within immune cells exhibit state-like properties that vary by >20-30% week-to-week. These findings warrant for future studies investigating the factors that drive these changes. We had previously observed that up to 12% of the inter-individual variation in MHI was attributable to positive mood Importantly, we note that our results demonstrate mitochondrial changes taking place within less than one week. Therefore, establishing the exact temporal dynamics of leukocyte mitochondrial variations will require repeated assessments with even greater temporal resolution.
A particularly striking observation is that the dynamic changes in cell subtype-specific mitochondrial features within a person can, in some cell types, be of similar magnitude to the biological variation observed among a heterogenous reference cohort of 21 individuals composed of women and men spanning four decades of life. Thus, a single measure of mitochondrial function may not accurately reflect a given person's average mitochondrial functional capacity.
To account for naturally occurring within-person variation in mitochondrial biology, as for other biological markers with high intra-person variation (e.g., cortisol (Segerstrom et al., 2017)), our result highlights the importance for future research of repeated-measures designs to capture statistically stable inter-individual differences.
Our mitotype approach, while exploratory, also provided further insight on the mitochondrial functional specialization among circulating human immune cell subtypes. Most notably, the innate and adaptive subdivisions, as well as the metabolic transition from naïve T lymphocytes to memory states, both harbored distinct mitotypes. Here also, we found mitotypes of specific cell subtypes to be relatively conserved between our heterogenous cohort and repeat participant. Thus, the mitotype approach may help to achieve an understanding of both quantitative and qualitative differences in mitochondrial phenotypes. Moreover, the mitotype analysis revealed potentially generalizable sex-and age-related differences in mitochondrial features.
In relation to sex differences, animal studies have consistently identified sexually dimorphic mitochondrial features, including greater mitochondrial content in females (reviewed in Consistent with previous findings, our data show similar changes in enzymatic activities for most, but not all, cell types, highlighting that the magnitude of sex differences is likely cell-type specific. . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; 25 Therefore, the use of methods offering a sufficient level of biological specificity appear warranted to reproducibly and accurately quantify these sex differences in different contexts. replication in response to bioenergetic dysfunction. The only cell type examined that did not exhibit an age-related positive association with mtDNAcn are CD8 + naïve T cells, which, interestingly, is the only cell type whose abundance in circulation significantly declines with advancing age. The basis for this correlation requires further investigation.
Because PBMCs are widely used across fields, mostly due to their ease of collection, we wanted to examine whether they would reflect or mask observations in specific cell subtypes. In many cases, robust associations in specific cell subtypes were either not observed or blunted in PBMCs. For example, there was no correlation between age and PBMCs MHI either in this study or previously (Karan et al., 2020), but correlations were present in purified cell subtypes.
Interestingly, the mitotype analysis revealed similar mitotypes between total PBMCs and cells of the innate subdivision, namely neutrophils, NK cells, and monocytes (often aligned along the same mitotype diagonal space on mitotype plots). In the mitotype plots, if PBMCs were composed . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 26 uniquely of a mixture of lymphocytes and monocytes, the natural expectation is that PBMCs would lie somewhere between the specific subsets that compose it. Instead, PBMCs, occupy an entirely different and unexpected mitotype space, indicating the presence of a contaminating factor. The platelet depletion experiment leaves little doubt that platelet contamination skews the measurements of several mitochondrial features, with some features being apparently more affected than others. Although we cannot entirely rule out potential contamination of individual cell types with residual platelets, the FACS labeling, wash, and sorting procedures must produce the purest sample with the highest degree of biological specificity.
This study presents limitations that should be noted. First, we acknowledge the relatively small sample size of the cohort, and the exhaustive repeated-measures in only one participant.
We included an equal representation of women and men in the cohort, but grouping by sex further reduced the sample size for subgroup analyses and may have led to difficulty identifying statistically significant results. Additionally, because our mitochondrial phenotyping platform currently requires ~5 million cells per sample, we only collected the six most abundant cell subtypes from each participant, which in some instances reduced the final sample size for different cell subtypes. However, we provide evidence for the variable proportions of cell subtypes from person-to-person, which determined which cell subtypes were collected from each participant. In order to accommodate the minimum cell number per sample, central and effector memory subtypes were grouped, although they may exhibit differences not examined here.
Furthermore, we recognize that additional cell surface markers may be useful to identify cell populations not included here (e.g. activated or adaptive lymphocyte subtypes). Finally, we did not test participants for CMV status, which may have provided additional insight into the agerelated effects on immune cell subtypes.
Altogether, these results define mitochondrial phenotypes among circulating immune cell subpopulations in humans and provide evidence that these features are in part influenced by sex and age. The repeated-measures of mitochondrial content and function in the same person also provide insight into the natural within-person variation of cell subtypes and dynamic mitochondrial profiles from week-to-week. Additionally, our mitotype approach identifies conserved multivariate phenotypic distinctions between lymphoid and myeloid cells, and naïve-to-memory lymphocyte states. Finally, our correlative PBMCs results in the cohort and repeat participant, together with the effects of active platelet-depletion, invite caution in the use and interpretation of mitochondrial measurements from cell mixtures such as PBMCs. Further research into the dynamic, state-like . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 27 mitochondrial properties of immune cell subtypes is necessary to understand both the causes of these variations, and their implications on the immune system and on health outcomes.
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Participants
The study was approved by New York State Psychiatric Institute (Protocol #7618). All participants provided written informed consent. Healthy adults between 20 and 60 years willing to donate blood were eligible for inclusion. Exclusion criteria included severe cognitive deficit, symptoms of flu or other seasonal infection four weeks preceding the visit, involvement in other clinical trials, malignancy or other clinical condition, and diagnosis of mitochondrial disease.
Participants were recruited via flyers posted in the Columbia University Irving Medical Center (CUIMC) community. Participants completed a short demographic questionnaire, anthropometric measurements (weight, height, heart rate, and blood pressure), and a blood draw. The main study cohort included twenty-one individuals (11 women, 10 men), mean age 36 ±11 (SD, range: 23-57), were recruited. The ethnic breakdown of our sample was: 2 African Americans, 7 Asians, and 12 Caucasians. All blood draws took place in the morning between 9-10am. Additionally, repeated weekly measures were collected across 9 weeks from one healthy Caucasian man (author M.P., 34 years old) to assess within-person variability in mitochondrial measures and immune cell type distribution. To standardize and minimize the influence of nutritional and behavioral factors in the repeat participant, repeated measures were collected at the same time (9:00am), on the same day of the week (Fridays), after a standardized breakfast (chocolate crepe), ~30-60 minutes after a regular bicycle commute to study site.

Blood Collection
A total of 100 ml of blood was drawn from the antecubital vein for each participant and included one EDTA tube (Cat# BD367841, 2 ml) for complete blood count (CBC), two SST coated tubes (Cat#BD367986, 5 ml) for hormonal measures and blood biochemistry in serum, and 11 Acid Dextrose A (ACD-A) tubes (Cat# BD364606, 8.5 ml) for leukocyte isolation and mitochondrial analyses, in order of collection. All tubes were gently inverted 10-12 times immediately after draw to ensure proper mixing. The EDTA and SST tubes for hematological and blood biochemistry were processed by the Center for Advanced Laboratory Medicine (CALM) Lab, and the ACD-A tubes were further processed for cell isolation. . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

PBMCs and leukocyte isolation
The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 29 (HBSS/BSA/EDTA), and FBS (Thermofisher, cat# 10437036) were brought to room temperature overnight. PBMCs were isolated on 15 ml of low density Ficoll 1077 in a 50 ml conical tube, and total leukocytes were separated on 15ml of higher density Ficoll 1119 distributed across 7 conical tubes. Blood was first pooled and diluted with HBSS/BSA in a 1:1 ratio, and 25 ml of diluted blood was carefully layered on Ficoll and then centrifuged immediately at 700 x g for 30 minutes (no brake) in a horizontal rotor (swing-out head) tabletop centrifuge, at room temperature.
Immediately after centrifugation, cells at the interface were collected and washed in 50 ml HBSS/BSA and centrifuged at 700 x g for 10 minutes. Supernatants were discarded and leukocyte pellets washed again in HBSS/BSA/EDTA and centrifuged at 700 x g for 10 minutes to limit platelet contamination. Low concentration EDTA (1mM) was used to prevent cell-cell adhesion or platelet activation, but a higher concentration was not used to avoid perturbing downstream mitochondrial assays.
To perform cell count, both i) PBMCs (1:10 dilution) and ii) total leukocytes (1:100 dilution) were resuspended in 1 ml of HBSS/BSA/EDTA and counted on the Countess II FL Automated Cell Counter (Thermo Fisher Scientific, Cat# AMQAF1000) in a 1:1 dilution with trypan blue.
Counts were performed in duplicates. If the difference between duplicates >10%, the count was repeated and the grand mean of the cell counts taken. Pellets of 5 million PBMCs were aliquoted and frozen at -80 o C for mitochondrial assays.

Immunolabeling of cell surface markers
Two antibody cocktails meant for i) cell counting (Cocktail 1) and ii) cell sorting (Cocktail 2), were prepared for fluorescence-activated cell sorting (FACS) (see Supplemental Table 4 for details). Antibodies were gently mixed, kept at 4°C, and shielded from light to avoid bleaching.
Cocktail 1 (containing cell surface markers for activated T lymphocytes) was prepared with 17.5 ul HBSS/BSA/EDTA and 2.5 ul per antibody (13 markers, 32.5 ul total antibody mix), for a total of 50 ul. Cocktail 2 was prepared with 200 ul HBSS/BSA/EDTA and 25 ul per antibody (12 markers, 300 ul total sorting antibody mix), for a total of 500 ul.
Prior to each study visit, cell collection tubes (Cat#: 352063, polypropylene, 5 ml) were coated with 4.5ml of DMEM/10% FBS media to minimize cell-tube adhesion and maximize the recovery of sorted cells. Tubes were incubated for 24 hours at room temperature and stored at 4°C until use, and decanted prior to use. Two coated polypropylene tubes were used for the FACS-ready antibody-labeled leukocytes, and an additional 60 coated polypropylene falcon tubes were decanted and 500 ul of media (DMEM/10% FBS) was added to receive sorted cells.
. CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint Prior to immunolabeling, total leukocytes were incubated with blocking buffer (to block non-specific binding to FC receptors) at a 1:10 dilution and incubated at room temperature for 10 minutes. A 2 million cell aliquot was diluted to a final volume of 100 ul with HBSS/BSA/EDTA and combined with 50 ul of Cocktail 1. The remainder of total leukocytes (~100M cells) were incubated with 500 ul of Cocktail 2 for 20 minutes in the dark, at room temperature. Both cell preparations were then washed with 5 ml of HBSS/BSA/EDTA and centrifuged at 700 x g for 5 minutes. Using the propylene tubes, Cocktail 1 cells were resuspended in 200 ul of HBSS/BSA/EDTA, and total leukocytes for FACS were resuspended to final concentration of 20 million cells/ml with HBSS/BSA/EDTA.

Fluorescence-activated cell sorting (FACS)
Cells labeled with the Cocktail 1 (counting) panel was only used for data acquisition and phenotype analysis. Cells labeled with the Cocktail 2 (sorting) panel was FACS sorted using a BD™ Influx cell sorter to isolate the subpopulations from peripheral blood. The sorter was controlled using BD FACS Sortware. Cells were sorted using 100 um size nozzle and under the sheath pressure of 20 psi. Sorting speed was kept around 11,000-12,000 events/second. Cell concentration for sorting was measured at about 15x10 6 cells per ml. Cell sorter drop frequency was 37 KHz, stream focus was 10%, maximum drop charge was 110 Volts. A six-way sorting, 1.0 drop pure sort mode was used to sort the cell subpopulations. Stream deflections were -84, -65, -32, 32, 65, and 84 for six-way sort from left to right. For each participant, 1 million cells (Cocktail 1 panel) were run first to calculate the potential yield of each subpopulation, including neutrophils, B cells monocytes, NK cells, naïve CD4 + and CD8 + , CM CD4 + and CD8 + , EM CD4 + and CD8 + , and TEMRA CD4 + and CD8 + (total cell number x percentage of each subpopulation). The six most abundant subpopulations were sorted. Purity checks were performed on all sorted subpopulations to ensure the instrument performance was good enough to reach the sorted population purity >95%. Raw data (.fcs file) was exported for further analysis on FCS Express 7 research version.

Processing and storage of sorted cells
Following flow cytometry, sorted cell subtypes were transferred and pooled by pipetting about half of each collection tube (2.5 ml) into larger falcon tubes, gently vortexing to liberate cells that may have adhered to the tube wall, and the remaining volume pipetted into the transfer tube.
HBSS/2% BSA was used as necessary to equilibrate and cells were centrifuged at 1,000 x g for 5 minutes. Following centrifugation, each cell pellet was isolated by gently decanting the supernatant and re-suspended into 1ml of HBSS/2% BSA. The resulting purified cell suspensions were transferred to a 1.5 ml Eppendorf tube for each cell type, centrifuged at 2,000 x g for 2 . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint 31 minutes at 4°C, and the supernatant carefully removed to leave a dry cell pellet. Samples were stored in liquid nitrogen for 4-12 months (-170°C) until mitochondrial biochemistry and mtDNAcn analyses were performed as a single batch.

Mitochondrial enzymatic activities
Samples were thawed and homogenized in preparation for enzymatic activity measurements with one tungsten bead and 500 ul of homogenization buffer (1 mM EDTA, 50 mM Triethanolamine). Tubes were transferred to a pre-chilled rack and homogenized using a Tissue Lyser (Qiagen cat# 85300) at 30 cycles/second for 1 minute. Samples were then incubated for 5 minutes at 4°C, and homogenization was repeated for 1 minute and the samples were returned to ice ready for enzymatic assays.   Table 3).

Platelet depletion in PBMCs
For this experiment, participants were 9 community-dwelling older adults (mean age = 79, For platelet depletion, PBMCs were first thawed at room temperature and centrifuged at 500 x g for 10 minutes. The supernatant was discarded and the cells were resuspended in 2 ml . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Counter (Thermo Fisher Scientific, Cat# AMQAF1000) in a 1:1 dilution with trypan blue. Cells were then divided into 2 aliquots: 1) 10 million cells for total PBMCs; 2) 11 million cells for platelet depletion. The total PBMCs were centrifuged at 2,000 x g for 2 min at 4 o C and subsequently frozen as a dry pellet at -80 o C until processing for enzymatic assays and qPCR. The PBMCs destined for platelet deletion cells were processed immediately.
The total PBMCs cell preparation was first immunolabeled with magnetically-coupled antibodies against the platelet marker CD61. The 11 million platelet-depleted PBMCs were then centrifuged at 300 x g for 10 minutes. After spin, the supernatant was aspirated and cells were total PBMCs, 2) platelet depleted PBMCs, and 3) enriched platelets. Each sample was processed in parallel for RC enzymatic activity assays and mtDNAcn as described above.

Statistical analyses
To adjust for potential order effects across the 340 samples (31 samples per 96-well plate, 17 plates total) a linear adjustment was applied to raw values enzymatic activity measures, which adjusts for potential storage and batch effects, ensuring consistency between the first and last samples assayed. Samples from both the cohort and repeat participant were processed and analyzed as a single batch.
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Data Availability
Further information and requests for resources should be directed to and will be fulfilled by the corresponding author. The raw datasets generated and analyzed in this study are available upon reasonable request.         (a) Overview of the repeat participant design, including blood collection, processing, and analysis.
All samples were collected, stored, and processed as a single batch with samples from the cohort.   . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020.       . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
. CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 16, 2020. ; https://doi.org/10.1101/2020.10.16.342923 doi: bioRxiv preprint