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[Preprint]. 2025 Oct 15:2025.10.15.682616. [Version 1] doi: 10.1101/2025.10.15.682616

Changes in cell size occur across brain regions in the aging Drosophila brain

Deena Damschroder 1,*, Laura Buttitta 1,*
PMCID: PMC12632749  PMID: 41279568

Abstract

Aging is accompanied by complex cellular changes in the brain, yet the interplay between cell size dynamics and ploidy, or DNA content during physiological aging is not well understood. Here, we employed flow cytometry to quantitatively analyze changes in cell size and DNA content in the aging Drosophila brain across specific brain regions, comparing the optic lobes (OLs) and central brain (CB). Our results reveal a large heterogeneity in brain cell size that is maintained during aging, despite an overall shift towards smaller cell sizes. This shift was observed early in the aging process and occurs similarly in OLs and CBs, with the OLs consistently exhibiting significantly smaller soma sizes than the CB. Simultaneously, we detected a significant, age-dependent accumulation of polyploid cells (cells with >2C DNA content), which was mostly evident in the OLs. Notably, although the polyploid cells are larger than diploid cells, the increase in polyploid cells was insufficient to counterbalance the age-related decline in cell size. This is in part because the average size of the polyploid cells also exhibited a decrease in size in the CB with age. We suggest that polyploidization exhibits region-specific differences with age, but that the age-associated loss of larger cells and cellular atrophy occurs across the brain and in polyploid as well as diploid cells. This work provides a foundation for future investigations into the mechanisms and consequences of age-dependent cellular shifts in the brain.

Introduction

Over the past two decades, our understanding of the Drosophila melanogaster brain has expanded considerably. High-resolution electron microscopy with machine learning has enabled the generation of volumetric reconstruction and analysis of an adult female brain (Zheng et al., 2018), and a complete connectome (Dorkenwald et al., 2024). These studies have provided insights into the structural organization of the fly brain and quantitative estimates of neuronal and synaptic counts. However, due to the technical complexity and resource-intensive nature of these approaches, age-related changes in connectome structure, cell size, and cell number remain unexplored using these high-resolution methods. In addition to the first complete fly connectome, single-cell and single-nuclei transcriptomic analysis has generated brain atlases that capture age-related gene expression changes (Davie et al., 2018; Lu et al., 2023). Between these brain atlases there are common features that have emerged including a reduction in gene transcripts and a reduction in recovered cell or nuclei number with age (Davie et al., 2018; Lu et al., 2023). With the current data and technology available, there is structural, synaptic, and cellular composition knowledge known about the young fly brain (Dorkenwald et al., 2024; Mu et al., 2021; Zheng et al., 2018) and gene expression data for the aging fly brain at later timepoints (e.g. 50 days) (Davie et al., 2018; Li et al., 2022; Lu et al., 2023). There is a gap in our knowledge about how the cellular composition of the Drosophila brain changes during healthy physiological aging and at what ages changes first begin to occur. We have described an accumulation of polyploid cells (cells with >2C DNA content) in the aging fly brain under normal physiological conditions (Nandakumar et al., 2020). Importantly this accumulation includes multiple cell types and begins early during the aging process, when fertility decline and deficits in learning and memory become evident, but weeks before significant mortality occurs (Damschroder et al., 2024). Polyploid cells can arise in tissues through altered cell cycles that lack mitosis or cell fusion (Nandakumar et al., 2021; Ren et al., 2020; Wu et al., 2022). Polyploid cells in the fly are often larger, and stress resistant to compensate for cell loss (Losick, 2016). While their roles in the aging brain are poorly understood, they are present in the aging brains of multiple organisms, including humans (Ippati et al., 2021; Mosch et al., 2007; Sigl-Glockner & Brecht, 2017), suggesting they may serve conserved functions with age.

To facilitate quantitative measurements of relative cell numbers, size and DNA content in the fly brain across age, we developed a flow cytometry protocol, that can be used on small numbers of dissected brains. We applied this approach across ages and even brain regions, subdividing the optic lobes from the central brain. Our protocol is rapid, cost-effective, and reduces sample loss by omitting centrifugation and filtration steps typically associated with traditional single-cell or nuclear isolation preparations. In addition, we confirmed our approach with imaging cytometry and in the process uncovered a significant population of small cells in the optic lobes that may be easily missed with standard flow cytometry thresholds. Thus, our approach allows us to recover as much of the cellular heterogeneity in the fly brain as possible.

Here we show that across the aging fly brain cell and soma size is highly heterogeneous, exhibiting multiple orders of magnitude in differences. With age, we find that there is a shift towards smaller cell sizes within the brain, and that this reduction in cell size occurs as early as 3 weeks of age, a timepoint in the adult fly lifespan when aging hallmarks are only first becoming apparent. We confirmed that the reduction in cell size with age occurs in the optic lobes as well as central brain and occurs in diploid as well as polyploid cells. We suggest that this may reveal a process of widespread cellular atrophy in the aging fly brain that occurs much earlier than the declines in gene expression and metabolic activity reported in the existing aging fly brain transcriptome datasets.

Methods:

Fly Husbandry:

The strain w1118 (BDSC 5905) was used for all experiments. Virgin male and female flies were collected within 24-hours of eclosion to ensure flies were age matched. Virgin male and female files were housed in separate vials (n=20) at 25°C on Bloomington Cornmeal recipe and exposed to 12-hours of ambient light per day (https://bdsc.indiana.edu/information/recipes/bloomfood.html).

Drosophila Brain Dissections and Flow Cytometry:

All Drosophila brains (omitting the ventral nerve cord) were dissected within 15 minutes prior to dissociation for flow cytometry analysis. For figure 1 and supplementary figure 1, whole brain preparations included both optic lobes (OLs) and central brain (CB), with each biological replicate containing cells from one male and one female brain. For regional analyses, OL samples contained 4 OLs (2 male, 2 female), while CB samples contained 2 CBs (1 male, 1 female). All datasets include eight biological replicates.

Figure 1: The aging Drosophila brain exhibits changes in cell sizes and cellular DNA content.

Figure 1:

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(A.) Representative flow cytometry density plot of single cells from one biological replicate, with FSC-H (cell size; y-axis) plotted against DyeCycle Violet fluorescence height (DNA content; x-axis). (B.) The average cell size decreases from day 7 to day 21 (t-test p = 0.017). (C.) Cell size distribution (% of max) for one representative biological replicate at day 7 (grey) and day 21 (yellow). The day 21 population shows a consistent shift toward smaller cell sizes across biological replicates. (D.) The same flow cytometry plot shown in (A) as a dot plot with a gate (orange) highlighting cells with >2C DNA content, defined as polyploid cells. Cells in purple are diploid cells. (E.) The proportion of polyploid cells increases in the whole brain from day 7 to day 21 (t-test p < 0.0001). (F.) Pairwise comparisons at day 7 and day 21, revealed the average polyploid cell size (a.u., FSC-H) is significantly larger than the average diploid cell (t-test p < 0.0001). Bar graphs display 8 biological replicates, with the ± SEM (E.) or the % coefficient of variation (CV) (B. & F.). Each biological replicate contains cells from 1 male and 1 female whole brain, which included the optic lobes (OLs) and central brain (CBs). A Tukey’s multiple comparison’s test was performed when appropriate. All significant differences are noted: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Flow cytometry experiments were performed using an Attune NXT flow cytometer or an Attune CytPix imaging flow cytometer. Both machines have the capabilities of detecting particles ranging from 0.05 μm to 50 μm. Regaurdless of instrument used, sample preparation was performed as previously described (Damschroder et al., 2024; Nandakumar et al., 2020). After dissection, brains were dissociated using trypsin::EDTA and stained with DyeCycle™ Violet for DNA content analysis and propidium iodide (PI negative), which was used to assess cell viability. For all samples, cell viability (PI negative cells) was >95%. Samples were incubated for twenty minutes protected from light, triturated with a p200 pipette tip, and finally transferred to tubes containing 400 μL trypsin::EDTA-PBS-dye solution. After an additional forty-five-minute incubation at room temperature protected from light, 500 μL 1X PBS was added and samples were gently vortexed prior to flow cytometric analysis. A notable feature of this protocol is there are no spin down or filtration steps, which helps ensure small or extremely large cells are not lost in the sample preparation.

To identify single cells, we used a strict gating strategy based on our previous work (Damschroder et al., 2024; Nandakumar et al., 2020 2024), with some minor modifications. The gating strategy for identify single cells is outlined in supplementary figure 1 for the whole brain and for the regional data in supplementary figure 2. Briefly, single cells re identified by gating events positive for DyeCycle™ violet staining. To eliminated doublets, the DNA-stained eventers were gated based on area (VL1-A) and height (VL1-H) (Rico et al., 2023; Shapiro, 2003). Using an imaging cytometer, Attune CytPix, we randomly imaged over 1,500 cells per biological sample and analyzed 10,865 pictures to confirm our gating was accurate. When all images from the polyploid gate (SF 1A., R5) were pooled together, we saw that 78% of the images taken were of single, non-doublet cells with elevated DyeCycle™ Violet fluorescence supporting the accuracy of our gating strategy. Through evaluation of our gating strategy using imaging cytometry, we discovered our previous FSC threshold settings used in our 2020 report exclude a proportion of very small, live diploid cells in the optic lobes (SF1A.). Due to their small size, these cells are not measured using default instrument FSC threshold settings. The reduced FSC threshold used here and inclusion of these small diploid cells reduces the relative percentage of polyploid cells : total cells in the optic lobes, but does not alter the increase in polyploidy with age in the optic lobes (Nandakumar et al., 2020).

Graphs and Statistics

Graphs and statistical analysis were created and performed using GraphPad Prism 10. For measurements of cell sizes, the coefficient of variation is shown. For graphs with statistical significance indicated, this was determined using an α-level of p < 0.05. The statistical analysis performed for all experiments are reported in the corresponding figure legends.

Results

The aging Drosophila brain maintains cell size heterogeneity while accumulating cells with increased DNA content.

Flow cytometry is a rapid, high throughput technique that captures multiple cellular characteristics. For the purposes of this study, we used flow cytometry to investigate changes in cell size and DNA content with age in the adult Drosophila brain (optic lobes and central brain). Our protocol is an unbiased survey of all cells in the brain and omits filtration and centrifugation steps to help minimize loss of small cells during sample preparation. Single cells were gated using Vybrant DyeCycle™ Violet (DCV) fluorescence height versus area (VL1-H vs. VL1-A; SF1A) and image flow cytometry confirms this gating strategy removes debris and isolates viable, single cells (SF1B.).

Forward scatter height (FSC-H) ranged from 5,000-750,000 a.u., representing the vast cell size heterogeneity in all samples (Fig. 1A.). Cell size heterogeneity within the Drosophila brain has also been previously reported using multiple imaging techniques, confirming that this heterogeneity is not an artifact of flow cytometry (Davie et al., 2018; Mu et al., 2021; Zheng et al., 2018). Analysis of the average cell size using FSC-H revealed a significant decrease in the cell size from day 7 to day 21 (Fig. 1B., t-test, p = 0.017). Examination of the size distribution (expressed as a percentage of maximum size) showed a distinct shift towards smaller cells at day 21 (Fig. 1C.). Prior work has also observed a reduction in cell size in the central fly brain with age, although much later at 50 days (Davie et al., 2018). Our results show the shift towards smaller size occurs early during the aging process and is already significant by three weeks of age. Despite this shift, the overall cell size heterogeneity in whole brains is maintained with age.

In parallel to cell size, analysis of DNA content by flow cytometry revealed a significant accumulation of polyploid cells (DNA content >2C) with age (Fig. 1, t-test p < 0.0001) which is consistent with previous reports (Damschroder et al., 2024; Nandakumar et al., 2020). We next examined the average cell sizes of polyploid cells, as polyploid cells in other Drosophila tissues and across organisms tend to be larger (Ren et al., 2020). Indeed, the average cell size of polyploid cells in the brain are greater than the average cell size for diploid cells (Fig. 1F., t-test p < 0.0001), but the small numbers of polyploid cells in the brain (between 4%-7% of all cells) means the increase in polyploid cells has a very small contribution to the overall cell size distribution with age. The increase in larger polyploid cells is thus offset by the much more prevalent and possibly widespread reduction in cell size across the aging brain.

Age-related changes in cell size occur across regions of the Drosophila brain.

After observing a shift towards smaller cell sizes in the aging brain, we decided to examine if this shift may be region-specific, for example in the central brain or optic lobes only. Using the same flow cytometry protocol and gating strategy (SF. 2A. & SF. 2B.), we examined cell size and DNA content of cell bodies in dissected, dissociated optic lobes (OLs) only or central brains (CB) only. Like the whole brain, there is a heterogeneous spread in cell sizes, along with clear regional differences, with the OLs containing many more small cells than the CB (SF. 2C., Fig. 2A. & 2B.), which may be often mis-assigned as debris and missed by default thresholds in flow cytometry. The average cell size in the OLs is significantly smaller than the average cell size in the CB (Fig. 2B., 2-way ANOVA, Region factor p < 0.0001). As seen for the whole brain, cell size decreased in both regions with age (Fig. 2B., 2-way ANOVA, Age factor p < 0.0001) indicating that both the CB and OLs exhibit significant changes in cell sizes by 21 days of age. Notably, there was a distinct small cell population in the OLs that was not present in the CB (Fig. 2D & Fig. 2E. 2-way ANOVA, Region factor, p < 0.0001). In the OL, this small population of cells comprises 7.4% of cells analyzed and significantly increases by 21 days to 10.5% (Fig. 2E., Tukey’s test p = 0.0079). Thus, the increase in small cells in the OLs with age also contributes to the shift towards smaller cells observed at the whole brain scale. This is notable because previous measurements of cell size decreases with age were limited to specific neuronal types in the central brain (Davie et al 2018). Our data suggests this is a widespread phenomenon across the central brain and optic lobes, that begins early in the aging process.

Figure 2: The optic lobes (OLs) contain smaller cells relative to the central brain (CB) and show a decrease in cell size with age.

Figure 2:

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(A.) Representative flow cytometry plots for OL and CB samples from one biological replicate, with FSC-H (cell size; y-axis) plotted against DyeCycle Violet fluorescence (DNA content; x-axis). (B.) The average cell size decreases with age and the OLs contain smaller cells than the CB (2-way ANOVA, Region factor p < 0.0001, Age factor p < 0.0001). (C.) An overlay of flow cytometry plots from the OL (black) and the CB (red) with FSC-H (cell size; y-axis) plotted against DyeCycle Violet fluorescence (DNA content; x-axis). The green gate represents a population of small cells (FSC-H, <12,000 a.u.). (D.) Cells <12,000 a.u. are more abundant in the OLs and increase with age (2-Way ANOVA, Region factor p < 0.0001, Age factor p< 0.0001). Graphs display data from 8 biological replicates ± SEM (D.) or the %CV (B.). OL replicates contain 4 OLs (2 male and 2 female) and CB replicates contain 2 CBs (1 male and 1 female). A Tukey’s multiple comparison’s test was performed when appropriate. All significant differences are noted: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Age differentially affects polyploid cell abundance and size between brain regions.

We also examined the distribution of polyploid cells (>2C DNA content) between the OLs and CBs to further characterize regional differences observed in cell size. Representative flow cytometry plots of OLs or CBs from 21-day old brains show polyploid cells in both brain regions (Fig. 3A), but the proportion of polyploid cells in the brain regions differ (2-way ANOVA, Region factor p < 0.0001), with 7-day old CBs containing more polyploid cells than the OLs (Fig. 3B., Tukey’s multiple comparisons test, p < 0.0001). However, by 21-days of age, there was no significant difference in the proportion of polyploid cells between the two brain regions (Fig. 3B., Tukey’s multiple comparisons test p > 0.05), suggesting the increase in the number of polyploid cells in the OLs is greater than that in the CBs. Indeed, little change is observed in the fraction of polyploid cells in the central brain with age at 21 days.

Figure 3: Aging impacts polyploid cell accumulation and size in a region-specific manner.

Figure 3:

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(A.) Representative flow cytometry dot plots of OL or CB cells showing FSC-H (size; y-axis) vs. DyeCycle Violet (DNA content; x-axis) with >2C cells gated in orange. (B.) The proportion of polyploid cells in the OLs is lower than the CB at day 7, but by day 21 there is no difference in the proportion of polyploid cells between the OLs and the CB (2-way ANOVA, Region factor p < 0.0001, Age facto p = 0.1127). (C.) The average size of polyploid cells in the CB become smaller with age (2-way ANOVA, Age factor p < 0.0001, Region factor p = 0.023). Graphs display data from 8 biological replicates, with ± SEM (B.) or the %CV (C.). OL only replicates contain 4 OLs (2 male and 2 female) and CB only replicates contain 2 CBs (1 male and 1 female). A Tukey’s multiple comparison’s test or Sidak’s test was performed when appropriate: *p < 0.05, **p < 0.01, ***p < 0.0001, ****p < 0.0001

Similar to the whole brain, the average size of polyploid cells is larger than diploid cells regardless of region (SF. 3A & 3B., 2-way ANOVA, DNA content factor p < 0.0001). Additionally, cells within the CB are larger than the OL regardless of DNA content (SF. 3A & 3B., 2-way ANOVA, Region factor p < 0.0001). During aging, the average size of polyploid cells in the CB is reduced (Fig. 3C., Sidak’s multiple comparison’s test p < 0.0058). We observe no difference in the proportion of polyploid cells within the small cell population with age (< 12,000 a.u.) identified in Fig. 2C. (SF. 4B., 2-way ANOVA, Aging factor p = 0.394; Region factor p = 0.147). This suggests that the reduction in cell size with age occurs across brain regions, in both diploid and polyploid cell populations and occurs much earlier than previously recognized. We suggest that the reduction in cell size is an early and widespread hallmark of aging in the Drosophila brain that can be easily and quantitatively measured across populations using flow cytometry.

Discussion

In this study, flow cytometry was used to survey the size and the DNA content of cells in the aging Drosophila brain. Our analysis reveals cell size variation is preserved in the aging brain, though there is a shift towards smaller cell size detected in the whole brain and at a regional level. Simultaneously, the proportion of large polyploid cells (>2C DNA content) increases in the whole brain and in optic lobes (OLs). These phenotypes highlight the complexity of the aging brain and the fact that there are a multitude of processes occurring simultaneously during aging.

Cell size heterogeneity is maintained during aging while large cells may be lost or atrophy.

Data from a 3D reconstructed, 5-day-old Drosophila brain generated from high-resolution electron microscopy images revealed innate regional cell size differences between the optic lobes and central brain (Mu et al., 2021). Given that cell loss occurs during brain aging and the intrinsic regional differences in cell sizes exist, we normalized our data by examining percentages relative to all single cells analyzed per sample to account for potential cell loss and inherent differences in cell numbers between the OL and CB regions.

Regardless of region (whole brain, OL, or CB), a shift in cell size towards smaller cells was observed from day 7 to day 21 (Fig. 1B. & Fig. 2B.). This result is consistent with multiple previous reports, although here we reveal that this occurs much earlier than previously appreciated. Brain region-specific cell loss occurs in healthy and diseased adult animals (Davie et al., 2018; Edler et al., 2020; Mortera & Herculano-Houzel, 2012; Mu et al., 2021; West et al., 1994). Specifically, in the Drosophila brain, single-cell and single-nuclei sequencing studies revealed reduced cell numbers, decreased gene expression during aging, and smaller cells in specific neuronal populations in the central brain (Pdfs, dFB, and OPN). Collectively this data supports a co-occurrence of cell loss and cell shrinkage with age in the fly brain (Davie et al., 2018; Lu et al., 2023). The relationship between cell size and selective neuronal vulnerability has been documented across multiple animal species, though this association is multifactorial and complex (Fu et al., 2018; Jackson et al., 2024; Saxena & Caroni, 2011). Although cell loss occurs in the aging brain, all neuronal populations remained detectable in sequencing datasets at older ages (Davie et al., 2018; Lu et al., 2023), which suggests that while size may contribute to increased cell death, it does not lead large cells to be significantly preferentially eliminated. Thus, we propose the reduction in larger cells likely results from increased cell death for a small subset of cells combined with cellular atrophy that causes large cells to shrink in size.

Human brain imaging studies have established that atrophy (cells reducing in size) occurs in both gray and white matter of healthy aging human brains, with a variety of proposed factors contributing to this tissue loss during aging (reviewed in (Blinkouskaya et al., 2021)). Observing and quantitatively measuring this atrophic phenotype in Drosophila, a well-characterized and relatively simple model organism, provides valuable opportunities to investigate the mechanisms underlying brain atrophy during aging. Furthermore, the observation that this an early event in the aging process possibility that large cells both atrophy and undergo cell death cannot currently be ruled out. Future studies investigating whether larger cells in aging wild-type Drosophila brains exhibit accelerated death rates or undergo atrophy will be critical for determining the precise mechanisms driving age-related brain volume loss and identifying potential therapeutic targets for combating neurodegeneration.

Polyploid cells accumulate in an age- and brain region-dependent manner.

Using newer imaging cytometry approaches, we re-examined our flow cytometry gating strategy and threshold settings for dissociated adult Drosophila brain tissue (SF. 1). We confirmed that our gating approach removes doublets, clumps and debris from our analysis and accurately identifies live, single cells. In this process we also discovered that significantly reducing the default size threshold traditionally used to remove debris from flow cytometry cell analysis revealed a population of small, diploid live cells, predominantly in the optic lobes, that we previously missed (SF. 1, Fig. 1A., & Fig. 2A.) (Nandakumar et al., 2020). The inclusion of the small diploid cells in our analysis reduces the relative percentage of polyploid vs. diploid cells in the optic lobes, but does not change the increase in polyploidy with age that we previously described (Damschroder et al., 2024; Nandakumar et al., 2020). Here, we show that including the small cells of the optic lobes corrects the relative percentages of polyploid cells in the brain such that the central brain, even at younger ages contains a higher percentage of polyploid cells than the OLs (Fig. 3B), but due to the age-associated increase in polyploidy in the optic lobes, both regions contain similar levels by day 21 (Fig. 3B.).

These regional differences in age-related polyploidization may reflect distinct stress resistance mechanisms between brain regions. Also, the discovery that polyploid cell size decreases in the CB but not the OLs (Fig. 3C.) may reflect different physiological roles for polyploid cells that is region specific and demonstrates that polyploidy may not be sufficient to protect against atrophy.

Collectively these findings demonstrate that the aging Drosophila brain is characterized by shifts toward smaller cell sizes in the whole brain, the optic lobes, and the central brain, suggesting that cellular atrophy and/or death of large cells represents a fundamental aspect of neurobiological aging in this model organism. These results provide important baseline data for future mechanistic studies investigating the cellular and molecular processes underlying brain aging.

Supplementary Material

1

Acknowledgments and Funding

The authors thank the members of the Buttitta lab for valuable advice. This work was supported by the National Institute on Aging Career Training in the Biology of Aging Training Grant (NIH T32 AG000114) and by the National Institute of General Medical Sciences of the National Institutes of Health (NIH R35 GM149273).

References

  1. Blinkouskaya Y., Caçoilo A., Gollamudi T., Jalalian S., & Weickenmeier J. (2021). Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev, 200, 111575. 10.1016/j.mad.2021.111575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Damschroder D., Sun J., McDonald K. O., & Buttitta L. (2024). Cell cycle re-entry in the aging Drosophila brain. bioRxiv, 2024.2008.2026.609689. 10.1101/2024.08.26.609689 [DOI] [Google Scholar]
  3. Davie K., Janssens J., Koldere D., De Waegeneer M., Pech U., Kreft L., Aibar S., Makhzami S., Christiaens V., Bravo Gonzalez-Blas C., Poovathingal S., Hulselmans G., Spanier K. I., Moerman T., Vanspauwen B., Geurs S., Voet T., Lammertyn J., Thienpont B.,…Aerts S. (2018). A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain. Cell, 174(4), 982–998 e920. 10.1016/j.cell.2018.05.057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Dorkenwald S., Matsliah A., Sterling A. R., Schlegel P., Yu S. C., McKellar C. E., Lin A., Costa M., Eichler K., Yin Y., Silversmith W., Schneider-Mizell C., Jordan C. S., Brittain D., Halageri A., Kuehner K., Ogedengbe O., Morey R., Gager J.,…FlyWire C. (2024). Neuronal wiring diagram of an adult brain. Nature, 634(8032), 124–138. 10.1038/s41586-024-07558-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Edler M. K., Munger E. L., Meindl R. S., Hopkins W. D., Ely J. J., Erwin J. M., Mufson E. J., Hof P. R., Sherwood C. C., & Raghanti M. A. (2020). Neuron loss associated with age but not Alzheimer's disease pathology in the chimpanzee brain. Philos Trans R Soc Lond B Biol Sci, 375(1811), 20190619. 10.1098/rstb.2019.0619 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Fu H., Hardy J., & Duff K. E. (2018). Selective vulnerability in neurodegenerative diseases. Nature Neuroscience, 21(10), 1350–1358. 10.1038/s41593-018-0221-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ippati S., Deng Y., van der Hoven J., Heu C., van Hummel A., Chua S. W., Paric E., Chan G., Feiten A., Fath T., Ke Y. D., Haass N. K., & Ittner L. M. (2021). Rapid initiation of cell cycle reentry processes protects neurons from amyloid-beta toxicity. Proc Natl Acad Sci U S A, 118(12). 10.1073/pnas.2011876118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Jackson W. S., Bauer S., Kaczmarczyk L., & Magadi S. S. (2024). Selective Vulnerability to Neurodegenerative Disease: Insights from Cell Type-Specific Translatome Studies. Biology (Basel), 13(2). 10.3390/biology13020067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Li H., Janssens J., De Waegeneer M., Kolluru S. S., Davie K., Gardeux V., Saelens W., David F. P. A., Brbic M., Spanier K., Leskovec J., McLaughlin C. N., Xie Q., Jones R. C., Brueckner K., Shim J., Tattikota S. G., Schnorrer F., Rust K.,…Zinzen R. P. (2022). Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly. Science, 375(6584), eabk2432. 10.1126/science.abk2432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Losick V. P. (2016). Wound-Induced Polyploidy Is Required for Tissue Repair. Adv Wound Care (New Rochelle), 5(6), 271–278. 10.1089/wound.2014.0545 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lu T.-C., Brbić M., Park Y.-J., Jackson T., Chen J., Kolluru S. S., Qi Y., Katheder N. S., Cai X. T., Lee S., Chen Y.-C., Auld N., Liang C.-Y., Ding S. H., Welsch D., D’Souza S., Pisco A. O., Jones R. C., Leskovec J.,…Li H. (2023). Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution. Science, 380(6650), eadg0934. doi: 10.1126/science.adg0934 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Mortera P., & Herculano-Houzel S. (2012). Age-related neuronal loss in the rat brain starts at the end of adolescence. Front Neuroanat, 6, 45. 10.3389/fnana.2012.00045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Mosch B., Morawski M., Mittag A., Lenz D., Tarnok A., & Arendt T. (2007). Aneuploidy and DNA replication in the normal human brain and Alzheimer's disease. J Neurosci, 27(26), 6859–6867. https://doi.org/27/26/6859 [pii] 10.1523/JNEUROSCI.0379-07.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Mu S., Yu S.-c., Turner N. L., McKellar C. E., Dorkenwald S., Collman F., Koolman S., Moore M., Morejohn S., Silverman B., Willie K., Willie R., Bland D., Burke A., Ashwood Z., Luther K., Castro M., Ogedengbe O., Silversmith W.,…Seung H. S. (2021). 3D reconstruction of cell nuclei in a full Drosophila brain. bioRxiv, 2021.2011.2004.467197. 10.1101/2021.11.04.467197 [DOI] [Google Scholar]
  15. Nandakumar S., Grushko O., & Buttitta L. A. (2020). Polyploidy in the adult Drosophila brain. Elife, 9. 10.7554/eLife.54385 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Nandakumar S., Rozich E., & Buttitta L. (2021). Cell Cycle Re-entry in the Nervous System: From Polyploidy to Neurodegeneration. Front Cell Dev Biol, 9, 698661. 10.3389/fcell.2021.698661 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ren D., Song J., Ni M., Kang L., & Guo W. (2020). Regulatory Mechanisms of Cell Polyploidy in Insects. Front Cell Dev Biol, 8, 361. 10.3389/fcell.2020.00361 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Rico L. G., Bardina J., Bistue-Rovira A., Salvia R., Ward M. D., Bradford J. A., & Petriz J. (2023). Accurate identification of cell doublet profiles: Comparison of light scattering with fluorescence measurement techniques. Cytometry A, 103(5), 447–454. 10.1002/cyto.a.24690 [DOI] [PubMed] [Google Scholar]
  19. Saxena S., & Caroni P. (2011). Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron, 71(1), 35–48. 10.1016/j.neuron.2011.06.031 [DOI] [PubMed] [Google Scholar]
  20. Shapiro H. M. (2003). Practical flow cytometry (4th ed.). Wiley-Liss. Publisher description http://www.loc.gov/catdir/description/wiley037/2002002969.html [Google Scholar]
  21. Table of contents. http://www.loc.gov/catdir/toc/wiley032/2002002969.html
  22. Sigl-Glockner J., & Brecht M. (2017). Polyploidy and the Cellular and Areal Diversity of Rat Cortical Layer 5 Pyramidal Neurons. Cell Rep, 20(11), 2575–2583. 10.1016/j.celrep.2017.08.069 [DOI] [PubMed] [Google Scholar]
  23. West M. J., Coleman P. D., Flood D. G., & Troncoso J. C. (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer's disease. Lancet, 344(8925), 769–772. 10.1016/s0140-6736(94)92338-8 [DOI] [PubMed] [Google Scholar]
  24. Wu C. S., Lu W. H., Hung M. C., Huang Y. S., & Chao H. W. (2022). From polyploidy to polyploidy reversal: its role in normal and disease states. Trends Genet. 10.1016/j.tig.2022.05.007 [DOI] [PubMed] [Google Scholar]
  25. Zheng Z., Lauritzen J. S., Perlman E., Robinson C. G., Nichols M., Milkie D., Torrens O., Price J., Fisher C. B., Sharifi N., Calle-Schuler S. A., Kmecova L., Ali I. J., Karsh B., Trautman E. T., Bogovic J. A., Hanslovsky P., Jefferis G., Kazhdan M.,…Bock D. D. (2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. Cell, 174(3), 730–743 e722. 10.1016/j.cell.2018.06.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

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