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. 2025 Aug 22;11(34):eadu2708. doi: 10.1126/sciadv.adu2708

Understanding monocyte-driven neuroinflammation in Alzheimer’s disease using human cortical organoid microphysiological systems

Chunhui Tian 1,†, Zheng Ao 1,†, Jonas Cerneckis 2,3, Hongwei Cai 1, Lei Chen 1, Hengyao Niu 4, Kazuo Takayama 5, Jungsu Kim 6, Yanhong Shi 2,3, Mingxia Gu 7, Takahisa Kanekiyo 8, Feng Guo 1,*
PMCID: PMC12372852  PMID: 40845091

Abstract

Increasing evidence strongly links neuroinflammation to Alzheimer’s disease (AD) pathogenesis. Peripheral monocytes are crucial components of the human immune system, but their contribution to AD pathogenesis is still largely understudied partially due to limited human models. Here, we introduce human cortical organoid microphysiological systems (hCO-MPSs) to study AD monocyte-mediated neuroinflammation. By culturing doughnut-shape organoids on 3D-printed devices within standard 96-well plates, we generate hCO-MPSs with reduced necrosis, minimized hypoxia, and improved viability. Using these models, we found that monocytes from AD patients exhibit increased infiltration ability, decreased amyloid-β clearance capacity, and stronger inflammatory response than monocytes from age-matched control donors. Moreover, we observed that AD monocytes induce pro-inflammatory effects such as elevated astrocyte activation and neuronal apoptosis. Furthermore, the marked increase in IL1B and CCL3 expression underscores their pivotal role in AD monocyte-mediated neuroinflammation. Our findings provide insight into understanding monocytes’ role in AD pathogenesis, and our lab-compatible MPS models may offer a promising way for studying various neuroinflammatory diseases.

Monocytes from Alzheimer’s disease patients induce disease phenotypes in human cortical organoid microphysiological systems.

INTRODUCTION

Alzheimer’s disease (AD) is the most common form of dementia that affects an estimated 44 million people worldwide by causing progressive memory and cognitive function deterioration (1). Increasing clinical evidence suggests that neuroinflammation functions as a vital factor in AD pathogenesis (2), as shown by neuroinflammatory changes in patients with AD through molecular imaging (3, 4) and postmortem brain analysis (5, 6). The excessive inflammatory activation of brain-resident immune cells in response to AD-related neuropathology is well studied and known to accelerate neurodegeneration (7, 8). However, the role of the peripheral immune system in AD-related neuroinflammation and neurodegeneration is still largely understudied (9, 10). Peripheral monocytes, a crucial component of the peripheral immune system, are gaining increasing recognition for their importance in inflammation (11, 12), which has generated substantial interest in their role in AD neuroinflammation and potential impact on disease progression. End point analyses of postmortem AD brain tissues show extensive monocyte infiltration into the brain parenchyma of patients with AD, where they cluster around amyloid-β (Aβ) plaques (13, 14). The population of infiltrated monocytes was also found to increase as the AD progression worsens (13). However, dynamic interactions of monocytes and brain parenchyma remain poorly understood in AD neuroinflammation, partly due to the absence of suitable human models.

Human brain organoids are three-dimensional (3D) in vitro brain-like tissues derived from human stem cells and hold a remarkable potential for modeling and understanding neurological disorders (15–18). With their 3D structures and cell components resembling the human brain, these organoids can replicate the complex brain microenvironment better than 2D in vitro models (15–17, 19), Meanwhile, being entirely derived from human cells, these organoids can fully replicate human biology and potentially reduce the translational barriers from experimental findings to therapeutic applications (20–22), offering advantages over animal models (23–25). In recent years, organoid technology has been extensively applied to model AD-related pathology and uncovers complex cell-cell interactions deteriorate as AD progresses (26). Organoids derived from genetically edited human pluripotent stem cells (hPSCs) or AD patients induced hPSCs (hiPSCs) have been generated to study risk gene factors in AD such as APP, PSEN1/2, and APOE4 mutations (27–29). In addition, human brain organoids derived from healthy hiPSCs and human embryonic stem cells (hESCs), have been cocultured with microglia or exposed to various noncellular factors, such as Aβ and AD serum, to model various AD-related pathologies (30–32). However, current human brain organoids are limited for studying AD neuroinflammation due to the current challenges such as high heterogeneity, low throughput, notable necrosis and hypoxia, and lack of immune components. Recently, microfluidics and organ chips have been used to engineer better organoids and assembloids using sophisticated microfabricated devices and complicated engineering systems (33–37). There is still a tremendous need of engineering devices, platforms, and technologies to develop simple, robust, scalable, and user-friendly organoid models for wide applications in common research lab settings.

Here, we report innovative human cortical organoid microphysiological systems (hCO-MPSs) for understanding monocyte-driven AD neuroinflammation. By incorporating 3D-printed devices into an adapted cortical organoid protocol, we can engineer and culture 96 hCO-MPSs within a commonly used 96-well plate, and each MPS consists of a doughnut-shape hCO and a 3D-printed device. Our MPS models may have several unique aspects. First, to better investigate AD neuroinflammation, we engineer MPSs with the unique doughnut-shape hCOs. They can not only enhance oxygen/nutrient diffusion to eliminate the necrotic and hypoxic conditions but also facilitate the incorporation of immunocytes (e.g., monocytes), compared to conventional spheroidal hCOs. Second, through incorporating with time-lapse imaging, our MPSs can enable the tracking of dynamic immune-organoid interaction in 3D human brain-like microenvironments in parallel. In addition, compared to current microfluidic and organ chip approaches, our MPSs are simple, robust, and scalable, due to the employment of simple 3D printing technology. Last, our MPSs are user-friendly and compatible with current organoid protocols that use well plates and orbital shakers in common research lab settings. Thus, our MPS models allow us to study dynamic immune-neuron interaction in a 3D human brain–like environment, especially to understand monocyte-mediated AD neuroinflammation.

RESULTS

hCO-MPSs for studying monocyte-mediated neuroinflammation in AD

To model interactions between monocytes and brain components during AD neuroinflammation, we developed hCO-MPSs, which integrate AD monocytes and doughnut-shape hCOs (Fig. 1A). AD monocytes, derived from the peripheral blood mononuclear cells (PBMCs) of patients with AD, carry patient-specific disease information. When integrated with cortical organoids generated from human iPSCs, which provide a 3D human brain microenvironment, the MPS allows us to investigate monocyte-mediated AD neuroinflammation. To mitigate necrosis and hypoxia within the organoids, we used unified 3D-printed devices to develop doughnut-shape hCOs. Those devices for generating doughnut-shape hCOs consist of a hollow circular scaffold with a mesh structure, enabling efficient culture medium perfusion and easy monocyte loading into the hCO (Fig. 1B and fig. S1). Combined with current organoid protocol (17), the embryoid bodies (EBs) grow on the perfusable scaffold, gradually fusing to form doughnut-shape hCOs (Fig. 1C and fig. S2). These doughnut-shape hCOs could be generated in a high-throughput manner since 3D-printed scaffolds are perfectly compatible with commonly used 96-well plates (Fig. 1D). Doughnut-shape hCOs contain mature microtubule-associated protein 2–positive (MAP2+) neurons and glial fibrillary acidic protein–positive (GFAP+) astrocytes as well as exhibit ventricular zone (VZ)–like tissue organization (Fig. 1E and fig. S3A). Furthermore, the doughnut-like shape as well as the shaking flow and the perfusable scaffold promote efficient nutrient and oxygen exchange, leading to significantly increased hCO cell viability on-device culture (Fig. 1F). Specifically, hCO-MPSs with the engineered doughnut-shape hCOs have an average ring width of 0.5 mm (fig. S3, B and C), effectively preventing hypoxia and necrosis in organoids (Fig. 1G). In contrast, a substantial portion of spherical hCOs that has an average radius of 0.9 mm (fig. S3, B and C) cannot be efficiently supplied with oxygen and nutrients due to diffusion constraints, resulting in the formation of an obvious hypoxic core (Fig. 1G).

Fig. 1. hCO-MPSs for understanding monocyte-mediated neuroinflammation in AD.

Fig. 1.

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(A) Schematics of modeling monocyte-mediated neuroinflammation in AD using hCO-MPSs. (B) The image of a 3D-printed device for generating doughnut-shape hCOs. Scale bar, 500 μm (C) A timeline showing doughnut-shape hCO generation in the MPS platform. Scale bar, 200 μm. (D) The 96-well-compatible hCO-MPS platform for doughnut-shape cortical organoid differentiation. (E) Whole-mount staining of 3-month-old doughnut-shape hCOs for GFAP (a mature astrocyte marker) and MAP2 (a mature neuron marker) (left) and 1-month-old doughnut-shape hCOs for beta III-tubulin (Tuj1, an early-stage neuron marker) and (sex determining region Y)-box 2 (SOX2, a neural progenitor marker) (right). The white dashed line indicates the ventricular zone (VZ)–like structure. Scale bars, 100 μm. (F) Quantification of cell viability in 3-month-old hCOs and hCO-MPSs (mean ± SEM, n = 5 organoids, from three independent experiments). (G) Quantification of hypoxia dye intensity in 3-month-old hCOs and hCO-MPSs.

To gain deeper insight into the differences between engineered doughnut-shape and conventional spherical hCOs, we conducted bulk RNA sequencing on day 90. Differentially expressed gene (DEG) analysis between engineered doughnut-shape and conventional spherical hCOs revealed significant transcriptional changes between the two organoid types (fig. S4A). Gene ontology (GO) enrichment analysis based on DEGs revealed up-regulation of pathways associated with multiple aspects of neuronal development and down-regulation of pathways related to cell cycle regulation, apoptosis, and stress response (fig. S4B). Specifically, the down-regulated gene expression of SOX2 and Tuj1 (TUBB3) (early development), as well as reduced cell cycle–related genes and pathways, along with the up-regulated gene expression of MAP2 and GFAP (mature neuron and astrocytes), suggested enhanced cellular maturation in the engineered doughnut-shape hCOs. Meanwhile, the higher expression of CTIP2 (BCL11B) and TBR1, canonical markers of the cortical plate, further supports improved development and greater cortical specificity in the engineered doughnut-shape organoids (fig. S4C). Thus, we engineered a MPS that supports efficient hCO differentiation and maturation, resolves hypoxia, and can be easily scaled for high-throughput experiments.

Infiltration study of monocytes from patients with AD and age-matched controls

We then created a physiologically relevant platform of monocyte infiltration in AD by using monocytes isolated from PBMCs of patients with AD and age-matched control (AC) donors (table S1). Before introducing AD monocytes into the system, we first incorporated the THP-1 monocyte cell line to assess the feasibility of our hCO-MPSs (fig. S5). The results highlighted the superior 3D brain-like microenvironment provided by the organoids (fig. S5, A and B), and the successful infiltration of THP-1 cells, especially in the condition that simulated the activated state of AD monocytes and the AD brain environment (lipopolysaccharide/interferon-γ prestimulated THP-1 cells in Aβ-pretreated doughnut-shape hCOs) (fig. S5, C to E). AD monocytes were then introduced into the hCO-MPSs to mimic monocyte infiltration into AD patient brains. Time-lapse images captured over 24 hours revealed gradual AD monocytes infiltration into the hCO-MPSs (Fig. 2A and movie S1). Compared to conventional spherical hCOs, nearly twice as many monocytes infiltrated the hCO-MPSs, highlighting the advantages of using our platform for modeling monocyte infiltration (Fig. 2B and fig. S3E). Enhanced monocyte infiltration into hCO-MPSs can be attributed to the higher surface area-to-volume ratio of doughnut-shape hCOs compared to spherical hCOs, thus increasing the contact area for monocytes (fig. S3D).

Fig. 2. Infiltration study of monocytes from patients with AD and AC donors.

Fig. 2.

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(A) Dynamic infiltration of AD monocytes (ADMos) into the hCO-MPSs over 24 hours. Scale bar, 100 μm. (B) Quantification of ADMo infiltration into conventional spherical hCOs and the hCO-MPSs with doughnut-shape hCOs over 24 hours (mean ± SEM, n = 5 organoids, from three independent experiments). (C) Representative images showing monocyte infiltration into the hCO-MPSs under different conditions: AC monocytes (ACMos) cocultured with hCO-MPSs, ACMo cocultured with Aβ-pretreated hCO-MPSs (ACMo + Aβ), ADMo cocultured with hCO-MPSs, and ADMo cocultured with Aβ-pretreated hCO-MPSs (ADMo + Aβ). Scale bar, 50 μm. (D) Quantification of monocyte infiltration shown in (C) (mean ± SEM, n = 5 organoids, from three independent experiments). (E) UMAP visualization of cell types in hCO-MPSs. (F) UMAP visualization of the cell distribution in ACMo-cocultured hCO-MPSs (ACMo + hCO-MPSs) and ADMo-cocultured hCO-MPSs (ADMo + hCO-MPSs). (G) Gene expression of CCR1 in ACMo and ADMo within hCO-MPSs, respectively. (H) Expression of cell infiltration–related genes in ACMo and ADMo within hCO-MPSs, respectively. Quantification of monocyte infiltration was performed on the bottom surface area of the organoids.

We next asked if monocyte infiltration into hCO-MPSs is influenced by disease status and AD-related neuropathology. To this end, we quantified the infiltration of monocytes derived from AC donors and patients with AD into hCO-MPSs. We found that AD monocyte infiltration into hCO-MPSs was significantly higher than that of AC monocyte, suggesting that monocytes in the AD patient blood have cell-intrinsic properties that promote their infiltration into the brain tissues (Fig. 2, C and D). Furthermore, Aβ treatment mimicking AD brain environment substantially increased the infiltration of AD monocytes, but not AC monocytes, into hCO-MPSs, underscoring the role of Aβ in peripheral monocyte infiltration in patients with AD. AD monocytes also colocalized with Aβ in the hCO-MPSs (Fig. 2C), supporting the previous observations that infiltrated monocytes accumulate around Aβ plaques in the brain tissues of patients with AD (13). To further investigate AD monocyte interactions with brain tissue, we performed single-cell RNA sequencing (scRNA-seq) of AD monocyte–enriched hCO-MPSs (ADMo hCO-MPSs) and AC monocyte-enriched hCO-MPSs (ACMo hCO-MPSs) (Fig. 2E and fig. S6A). Cell clustering confirmed higher infiltration of AD monocytes compared to AC monocytes (Fig. 2F). We detected increased expression of CCR1, an important receptor for monocyte recruitment, as well as a number of other cell migration–related genes including SEMA6B, FGR, APLP2, NF1, NUMB, PECAM1, CYP1B1, and FOXO3 (38–43) in AD monocytes as compared to AC monocytes (Fig. 2, G and H). In summary, the functional in vitro experiments and gene expression changes both revealed the distinct increased infiltration of AD monocytes into the brain-like tissue, which further enhanced in the presence of Aβ.

Altered functionality of AD monocytes in hCO-MPSs

Given our observation that AD monocytes colocalized with Aβ debris, we next investigated whether these monocytes can effectively phagocytose Aβ and thus assist brain-resident cells in clearing AD-related debris (44, 45). Upon closer inspection of monocyte-infiltrated hCO-MPSs, we detected an extensive colocalization of Aβ (green) and monocyte (red) signals in AC monocytes, whereas the overlap was reduced in AD monocytes (Fig. 3, A and B). These findings indicate that although monocytes can readily clear Aβ deposits, their clearing capacity is decreased in AD. We found that the expression of phagocytosis-related genes, including FCER1G, FCGR2A, TYROBP, and GAS6 (46–48), was down-regulated in AD monocytes as compared to AC monocytes (Fig. 3C), indicating a reduced ability of AD monocytes to phagocytose Aβ. Similarly, several genes reported to be involved in Aβ catabolism, such as RAB5A, RAB11A, CLU, and LRP2 (49–53), were also down-regulated in AD monocytes, suggesting the impaired Aβ catabolism in AD monocytes. Meanwhile, among the DEGs between AD and AC monocytes in hCO-MPSs, we found that S100A8 and LYZ were the most up-regulated genes in AD monocytes (Fig. 3D). Given their major roles in the immune response and inflammation (54, 55), the significant up-regulation of S100A8 and LYZ suggested an enhanced inflammatory response in AD monocytes. Consistent with our in vitro findings, GO enrichment analysis confirmed pathways related to elevated inflammatory response and cell migration, as well as reduced phagocytosis and Aβ metabolism in AD monocytes (Fig. 3E and fig. S6B). Overall, we found the altered functionality of AD monocytes in hCO-MPSs, including impaired Aβ clearance ability and increased inflammatory response.

Fig. 3. Functional characterization of ACMos and ADMos in hCO-MPSs.

Fig. 3.

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(A) Representative images showing Aβ phagocytosis by ACMo and ADMo within hCO-MPSs. Scale bars, 10 μm. (B) Quantification of Aβ phagocytosis shown in (A) (mean ± SEM, n = 5 organoids, from three independent experiments). (C) Expression of phagocytosis- and Aβ catabolism-related genes in ACMo and ADMo within hCO-MPSs. (D) A volcano plot displaying differentially expressed genes (DEGs) in ADMo compared to ACMo within hCO-MPSs. (E) Gene ontology (GO) enrichment analysis based on ADMo DEGs.

AD monocytes induce astrocyte activation in hCO-MPSs

Having observed enrichment for inflammatory pathway activation in AD monocytes, we hypothesized that infiltrating peripheral monocytes induce astrocyte activation in the brain tissue of patients with AD, which is a well-described pro-inflammatory phenomenon that contributes to AD progression (56, 57). To evaluate astrocyte activation in both conditions, we first performed immunostaining for GFAP since reactive astrocytes are known to exhibit elevated GFAP expression and enlarged cell bodies (Fig. 4A and fig. S7) (58). We also quantified the production of reactive oxygen species (ROS), as astrocytes are a major source of ROS in the brain and their production increases upon astrocyte activation (59, 60). AD monocyte cocultured hCO-MPSs showed significantly higher GFAP fluorescence intensity and enlarged area, along with increased ROS production compared to AC monocyte cocultured hCO-MPSs, which were further amplified in the presence of Aβ (Fig. 4, B to D). These findings support our hypothesis of heightened neuroinflammation under AD pathology. In agreement with our functional experiments, astrocyte gene expression analysis revealed up-regulation of inflammatory and ROS-related genes (Fig. 4E). GO enrichment analysis also indicated an elevated inflammatory response in astrocytes in AD monocyte cultures as compared to AC monocyte cocultures (Fig. 4F and fig. S6C). Meanwhile, enriched processes among the down-regulated DEGs suggested impaired astrocyte ability to support neurons in AD monocyte-cocultured hCO-MPSs (Fig. 4F).

Fig. 4. ADMos induce astrocyte activation in hCO-MPSs.

Fig. 4.

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(A) Representative images showing astrocyte activation in hCO-MPSs under different conditions: AC monocytes (ACMos) cocultured with hCO-MPSs, ACMo cocultured with Aβ-pretreated hCO-MPSs (ACMo + Aβ), ADMo cocultured with hCO-MPSs, and ADMo cocultured with Aβ-pretreated hCO-MPSs (ADMo + Aβ). Scale bar, 20 μm. (B) Quantification of GFAP fluorescence intensity for (A) (mean ± SEM, n = 5 organoids, from three independent experiments). (C) Quantification of GFAP+ area for (A) (mean ± SEM, n = 5 organoids, from three independent experiments). (D) ROS levels in hCO-MPSs under the conditions shown in (A) (mean ± SEM, n = 5 organoids, from three independent experiments). (E) Expression of immune response- and ROS-related genes in astrocytes within ACMo- and ADMo-cocultured hCO-MPSs. (F) Gene ontology (GO) enrichment analysis based on astrocyte DEGs in ADMo-cocultured hCO-MPSs.

AD monocytes induce neuronal apoptosis in hCO-MPSs

Cellular AD pathologies eventually converge on neuronal network degeneration and neuron cell death that led to cognitive decline (61, 62). Neuroinflammation may cause neuron cell death by disrupting the supportive microenvironment of the human brain (63, 64). Therefore, we investigated whether infiltrating monocytes could induce neuronal apoptosis in hCO-MPSs. Within 24 hours of AD monocytes coculture with hCO-MPSs, we observed a gradual destruction of the VZ-like structure accompanied by cell vacuolation, indicating the detrimental effects of AD monocytes on the brain tissue (Fig. 5A and movie S2). We performed the terminal deoxynucleotidyl transferase (dUTP) nick end labeling (TUNEL) assay to quantify neuronal apoptosis in hCO-MPSs (Fig. 5B and fig. S8). TUNEL analysis revealed significantly higher neuronal apoptosis in AD monocyte-cocultured hCO-MPSs as compared to AC monocyte cocultures, which was further increased in the presence of Aβ (Fig. 5C). The expression of cell apoptosis-related genes ATF4, JUN, GADD45G, ITGB1, RPS3, and ANP32A (65) was also significantly up-regulated in neurons within AD monocyte-cocultured hCO-MPSs (Fig. 5D), supporting the in vitro observations. Last, GO analysis revealed enrichment among up-regulated DEGs for apoptosis, DNA damage, and stress response processes, as well as enrichment among down-regulated DEGs for synapse organization, neuron development, and neuron projection extension processes (Fig. 5E and fig. S6D). Together, these findings indicate that AD monocytes can induce neuronal apoptosis and thus may contribute to neurodegeneration in AD.

Fig. 5. ADMos induce neuronal apoptosis in hCO-MPSs.

Fig. 5.

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(A) Apoptosis dynamics in ADMo-cocultured hCO-MPSs over 24 hours. (B) Representative images showing neuronal apoptosis in hCO-MPSs under different conditions: AC monocytes (ACMos) cocultured with hCO-MPSs, ACMo cocultured with Aβ-pretreated hCO-MPSs (ACMo + Aβ), ADMo cocultured with hCO-MPSs, and ADMo cocultured with Aβ-pretreated hCO-MPSs (ADMo + Aβ). White arrows indicate apoptotic neurons. (C) Quantification of neuronal apoptosis shown in (B) (mean ± SEM, n = 5 organoids, from three independent experiments). (D) Expression of neuronal apoptosis–related genes in neurons within ACMo- and ADMo-cocultured hCO-MPSs. (E) Gene ontology (GO) enrichment analysis based on neuronal DEGs in ADMo-cocultured hCO-MPSs. [(A) and (B)] Scale bars, 20 μm.

Key roles of IL-1β and CCL3 in AD monocyte–mediated neuroinflammation in hCO-MPSs

To identify the key cytokines involved in monocyte infiltration and monocyte-mediated neuroinflammation in AD, we compared differentially expressed cytokine-encoding genes in both hCO-MPSs cocultured with AD and AC monocytes, respectively (Fig. 6A, cytokine-encoding genes are marked by triangles). We found that IL1B and CCL3, gene markers of the pro-inflammatory cytokine Interleukin-1 beta (IL-1β) and the chemokine C-C motif chemokine ligand 3 (CCL3) were the most up-regulated in AD monocyte-cocultured hCO-MPSs. We validated our findings by enzyme-linked immunosorbent assay (ELISA) assays, which consistently revealed increased protein levels of IL-1β and CCL3 in the supernatant of AD monocyte and hCO-MPS cocultures as compared to that of AC monocyte cocultures (Fig. 6B and fig. S9). As a prominent chemokine that signals via the CCR1 receptor (that we found to be highly expressed in AD monocytes in Fig. 2G) and exhibits chemotactic activity for monocytes, CCL3 may promote increased AD monocyte infiltration into the hCO-MPSs as observed in our study. Furthermore, as a central pro-inflammatory cytokine, IL-1β may contribute to monocyte-mediated neuroinflammation in the AD brain. Neutralization experiments were performed to validate the specific roles of CCL3 and IL-1β in AD monocyte–mediated neuroinflammation. Neutralization of CCL3 resulted in a significant reduction in monocyte infiltration (Fig. 6, C and D), supporting its role in directing AD monocytes into the organoids. Furthermore, coculture with IL-1β and CCL3 neutralizing antibodies led to a marked decrease in astrocyte activation and neuronal apoptosis compared to the untreated condition (Fig. 6, E to H), indicating that blockade of these cytokines can substantially mitigate AD monocyte–induced neuroinflammatory responses. The above findings demonstrate that IL-1β and CCL3 are key mediators in monocyte-driven neuroinflammation in AD (Fig. 7).

Fig. 6. IL-1β and CCL3 play key roles in monocyte-mediated neuroinflammation in AD.

Fig. 6.

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(A) A volcano plot showing differentially expressed genes (DEGs) between the AD monocytes (ADMos)–cocultured hCO-MPSs and AC monocytes (ACMos)–cocultured hCO-MPSs. Cytokine-encoding genes were labeled with triangles and color-coded on the basis of Log2 fold-change values. (B) ELISA assay results for IL-1β and CCL3 concentrations from three paired monocytes isolated from different patients with AD and their respective AC donors (mean ± SEM, n = 3 repeats from each patient). (C) Representative images showing ADMo infiltration into the hCO-MPSs with or without CCL3 neutralizing antibody (NAb). (D) Quantification of monocyte infiltration experiment shown in (C) (mean ± SEM, n = 5 organoids, from 3 independent experiments). (E) Representative images showing astrocyte activation in hCO-MPSs under different conditions: blank, ADMo with IL-1β NAb, ADMo with CCL3 NAb, and ADMo. (F) Quantification of GFAP fluorescence intensity for (E) (mean ± SEM, n = 5 organoids, from three independent experiments). (G) Representative images showing neuronal apoptosis in hCO-MPSs under different conditions: blank, ADMo with IL-1β NAb, ADMo with CCL3 NAb, and ADMo. (H) Quantification of neuronal apoptosis shown in (G) (mean ± SEM, n = 5 organoids, from 3 independent experiments). Scale bar: 20 μm. Quantification of monocyte infiltration was performed on the bottom surface area of the organoids.

Fig. 7. The proposed pathway of ADMo-driven neuroinflammation.

Fig. 7.

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DISCUSSION

In this study, we investigate the pathogenic role of monocytes in AD neuroinflammation by using the engineered hCO-MPSs. These hCO-MPSs comprise doughnut-shape organoids on 3D-printed devices in 96-well plates, reduce necrosis and hypoxia, and facilitate immune cell infiltration. Using this model, we observed that AD monocytes exhibit enhanced infiltration, reduced Aβ clearance, and stronger inflammatory responses, as well as induce astrocyte activation and neuronal apoptosis, underscoring the exacerbating role of monocytes in AD neuroinflammation, with IL-1β and CCL3 as key mediators.

Based on our findings, we hypothesize that AD monocytes infiltrate into the brain parenchyma and interact with various brain-resident cells, especially neurons and astrocytes. The interactions between these cells contribute to an increased production of inflammatory cytokines and chemokines including IL-1β and CCL3, which enhance neuroinflammation and peripheral immune cell migration. The scRNA-seq data revealed that IL1B and CCL3 are expressed in astrocytes and monocytes in AD monocyte-cocultured hCO-MPSs, suggesting that both cell types may contribute to the production of IL-1β and CCL3 proteins, with monocytes being the predominant source (fig. S10). Notably, CCL3 is a strong chemoattractant for monocytes, and its receptor CCR1 is also significantly up-regulated in AD monocytes compared to AC monocytes (Fig. 2G). The CCL3/CCR1 axis is known to mediate monocyte recruitment during inflammation (66), underscoring its potential role in attracting more monocytes into the brain in AD and thus further exacerbating neuroinflammation. Meanwhile, IL-1β, a pro-inflammatory cytokine and key mediator of neuronal injury (67), may further activate astrocytes and contribute to neuronal degeneration. Thus, we propose that monocytes exacerbate neuroinflammation in AD mainly through IL-1β and CCL3 (Fig. 7). However, their causal relationships remain undetermined due to the limitations of the current model. Given their potential therapeutic implications for AD, the interconnections and possible causal relationships would be further explored in future studies.

The unique design of engineered scaffolds enhances oxygen and nutrient exchange across the organoid tissue, prevents hypoxia and necrosis of the organoid core, and supports peripheral monocyte infiltration and retention within the organoid. These advantages primarily stem from the increased surface-to-volume ratio, aligning with previous studies that used fibers to guide organoid development and improve surface-to-volume dynamics (68). Although other approaches, such as organoid vascularization (69) or slicing (70), have been applied to reduce organoid hypoxia and necrosis, our platform does not require introducing vascular cells or continued handling of individual organoids, and thus provides an easy-to-use system for differentiation of highly viable organoids. Furthermore, hCO-MPSs are compatible with time-lapse imaging, enabling the study of monocyte infiltration dynamics, whereas the 3D-printed scaffold device is easy to generate, user-friendly, and compatible with commonly used organoid differentiation protocols.

We specifically chose to use cortical organoids because cortical neuropathology is evident in AD and contributes to cognitive decline (71). Furthermore, several studies have shown that guided cortical organoid differentiation yields highly reproducible organoids as compared to unguided organoid differentiation (72–74). The use of primary human monocytes also has several advantages: (i) their human origin enables the study of human-specific disease phenotypes and molecular mechanisms, (ii) monocytes derived from patients with AD may contain cell-intrinsic properties specific to that patient, and (iii) monocytes derived from patients with AD and age-matched controls may exhibit age-relevant phenotypes, given that aging is the primary risk factor for AD (75, 76).

While the hCO-MPS model used in our study has several advantages, it also presents certain limitations. Firstly, the model does not include a functional BBB, which is important for regulating monocyte entry. Secondly, the model lacks essential cell components: brain glial cells involved in neuroinflammation such as microglia and oligodendrocytes, as well as peripheral immune cells implicated in AD including T cells and B cells. Furthermore, AD-related genotypes are not incorporated, preventing the model from exploring the deep pathology. A more comprehensive MPS model which integrates these components would provide a powerful and effective platform for studying neurological diseases.

In summary, we believe our study provides insights into the impact of monocytes on AD pathogenesis. Our MPS models are simple, robust, scalable, user-friendly, and compatible with current lab settings, and may show great promise for modeling neuroinflammation, developing new therapeutics for various neuroinflammatory conditions, and contributing to the treatment of neurodegenerative diseases.

MATERIALS AND METHODS

hESC culture

WA09 hESCs (lot number WB68167) were acquired from the WiCell Research Institute and used following the regulations of WiCell and Indiana University. WA09 cells were cultured in mTeSR Plus medium (STEMCELL Technologies) on Matrigel (Corning)–coated six-well plates. The medium was changed every 2 days. HESCs cells were maintained in an incubator with a constant temperature of 37°C and 5% CO2. Every 5 days, the cells were passaged using ReLeSR (STEMCELL Technologies).

Generation of hCOs

Cortical organoids were generated following the published protocol (17). In brief, on day 0, WA09 hESCs (9000 cells per well) were seeded in 96-well U-bottom microplates (Corning) in mTESR plus medium (STEMCELL Technologies) containing 1 μM dorsomorphin (R&D Systems) and 10 μM SB431542 (Stemgent) to fabricate EBs. Y-27632 (10 μM; SelleckChem) was added to the induction medium for the first 24 hours. On day 3, the medium was changed to the cortical organoid medium (COM) consisting of neurobasal (Life Technologies) with 2% Gem21 NeuroPlex (Gemini Bio-Products), 1% GlutaMAX, 1% nonessential amino acid (Life Technologies), 1% N2 NeuroPlex (Gemini Bio-Products), and 1% P/S (Life Technologies), supplemented with 1 μM dorsomorphin and 10 μM SB431542. COM was supplemented with fibroblast growth factor 2 (FGF-2; 20 ng/ml; Life Technologies) from day 10 to day 17 and with FGF-2 (20 ng/ml) and epidermal growth factor (20 ng/ml; PeproTech) from day 17 to day 24. On day 24, the medium was changed to organoid maturation medium consisting of COM supplemented with brain-derived neurotrophic factor (10 ng/ml), glial cell line–derived neurotrophic factor (10 ng/ml), NT3 (10 ng/ml; all from PeproTech), 200 μM ascorbic acid, and 100 μM adenosine 3′,5′-monophosphate (Sigma-Aldrich). From day 30, cortical organoids were transferred to six-well plates in COM and placed on an orbital shaker at 90 rpm. Cortical organoids were maintained with medium changes every 2 days.

Device design and fabrication

The device was designed using the AutoCAD software and then printed with a 3D printer (Form3B, Formlabs) using our well-developed protocols (77–81). The Formlabs Clear Resin V4 (Formlabs) was used as the printing material. Detailed design parameters of the device are described in fig. S1.

On-chip culture of hCO-MPSs

On day 3 of cortical organoid differentiation, EBs were carefully aspirated with a 1000-μl pipette and placed in the space between the mezzanine of the 3D-printed device and the glass bottom in a 96-well plate. Eight to 10 EBs were added into each well, and the EBs merged over time to form doughnut-shape hCOs. After 30 days, the glass bottom was removed, and the hCO-MPSs were transferred to six-well plate and placed on an orbital shaker at 90 rpm to enhance nutrient and oxygen exchange. The same cortical organoid differentiation protocol (17) was used for doughnut-shape hCOs.

Cell viability assay

Live/dead cell staining was performed to measure the cell viability of doughnut-shape hCOs and conventional spherical hCOs. Carboxyfluorescein diacetate succinimidyl ester (BioLegend) and ethidium homodimer-1 (Invitrogen) diluted at 1:1000 were used to label live and dead cells, respectively, following the manufacturers’ protocols. Imaging was performed on an inverted fluorescence microscope (Olympus IX-83). ImageJ (version 1.54f) was used to quantify cell viability.

Hypoxia measurement

Image-iT Green Hypoxia Reagent (Invitrogen) diluted at 1:1000 was used to detect hypoxic tissue of doughnut-shape hCOs and conventional spherical hCOs following the manufacturer’s protocol. Imaging was performed on an inverted fluorescence microscope (Olympus IX-83). ImageJ (version 1.54f) was used to quantify hypoxia fluorescence intensity.

Whole-mount staining of cortical organoids

Whole-mount staining was performed to demonstrate the shape and other characteristics of doughnut-shape hCOs. Organoids were first washed twice with 1× phosphate-buffered saline (PBS; Gibco) and fixed with 4% paraformaldehyde at room temperature for 1 hour. Fixed organoids were washed with 1× PBS three times for 10 min, followed by incubation with organoid blocking buffer [3% fetal bovine serum, 1% (w/v) bovine serum albumin, 0.5% Triton X-100, 0.5% Tween 20, and 0.01% (w/v) sodium deoxycholate in 1× PBS] at room temperature for 2 hours. After blocking, the organoids were incubated with primary antibodies (diluted in the blocking buffer) with gentle rocking at 4°C overnight. The organoids were then washed with PBST (0.1% Tween 20 in 1× PBS) five times for 5 min and incubated with secondary antibodies and 4′,6-diamidino-2-phenylindole (DAPI; Invitrogen) (diluted in the blocking buffer) with gentle rocking and protected from light at 4°C overnight. After incubation with secondary antibodies, the organoids were washed with PBST five times for 5 min and once with 1× PBS followed by dehydration with 50, 70, and 100% methanol or ethanol for 1 hour each. Murray’s clear (2:1 benzyl benzoate to benzyl alcohol) was used for tissue clearing of the dehydrated organoids. Detailed information of antibodies and their dilutions used in this study is provided in table S2.

Cryo-sectioning of organoids

For cryo-sectioning of hCOs, organoids were washed twice with 1× PBS and fixed with 4% paraformaldehyde at 4°C overnight followed by dehydration in 30% sucrose in 1× PBS (w/v) at 4°C overnight. Organoids were then transferred to a cryomold (Sakura Finetek) with O.C.T. compound (Thermo Fisher Scientific) and frozen on dry ice. Embedded organoids were sectioned using a cryostat (Leica), and 30-μm-thick slices were collected on Superfrost Plus slides (VWR International).

Immunofluorescence staining

The slides of cryo-sectioned organoids were washed with 1× PBS twice for 5 min to remove the O.C.T compound and submerged in the Antigen Retrieval Solution (Thermo Fisher Scientific) and boiled for 20 min. After the slides cooled down, they were rinsed with 1× PBS and incubated with a blocking buffer (5% fetal bovine serum and 0.3% Triton X-100 in 1× PBS) for 1 hour. Subsequently, the slides were incubated with primary antibodies (diluted in the blocking buffer) protected from light at 4°C overnight. After incubation, the slides were washed with 1× PBS five times for 10 min, followed by incubation with secondary antibodies (diluted in the blocking buffer) protected from light at room temperature for 2 hours. Last, the slides were washed with 1× PBS q times for 10 min and incubated with DAPI diluted in 1× PBS for 10 min. Detailed information of antibodies used in the experiment can be found in table S2.

THP-1 cell culture

The THP-1 cells (American Type Culture Collection) were used in preliminary experiments to establish the coculture system before using patients’ monocytes. The culture medium for THP-1 cells was RPMI 1640 (Gibco) supplemented with 10% fetal bovine serum (Gibco), 0.05 mM 2-mercaptoethanol (Sigma-Aldrich), and penicillin/streptomycin (100 U/ml; Gibco). The cells were maintained in a humidity incubator at 37°C temperature and 5% CO2. The cells were passaged when the cell density reached 1 × 106 cells/ml. The culture medium was changed every 2 days.

Time-lapse imaging

The dynamic processes of monocyte infiltration and cell apoptosis were recorded using the Olympus OSR Spinning Disk confocal microscope. Cortical organoids expressing ChR2-EYFP (derived from H9-CAG-ChR2-EYFP hESCs) were cocultured with monocytes (1 × 105 monocytes/organoid) labeled with the Vybrant Dil solution (Invitrogen) for time-lapse imaging experiments. Videos were created with Adobe Photoshop 2024.

Monocyte isolation from human PBMCs

PBMCs of patients with AD were isolated from whole blood obtained from Indiana University Medical School using SepMate-50 (IVD) (STEMCELL Technologies) following the manufacturer’s protocol. PBMCs of AC donors were purchased from STEMCELL Technologies. Monocytes were isolated from PBMCs by positive selection using CD14 MicroBeads UltraPure and MS Columns (Miltenyi Biotec Inc) following the manufacturer’s protocol. Typically, 1.5 × 105 monocytes can be isolated from 1 × 106 PBMCs. Demographic information of patients with AD and AC donors is provided in table S1.

Imaging and quantification of monocyte infiltration

To study monocyte infiltration, monocytes were labeled with the Vybrant Dil solution (Invitrogen) in serum-free RPMI-1640 (Gibco) and incubated at 37°C and 5% CO2 for 30 min. Labeled monocytes were washed twice, and 1 × 105 monocytes were seeded onto each hCO-MPS. In Aβ treatment experiments, hCO-MPSs were preincubated with 1 μM of HyLite 488–labeled Aβ (1–42) (AnaSpec) and incubated on a shaker overnight to form Aβ aggregates. Monocyte infiltration was imaged using the Olympus OSR Spinning Disk confocal microscope inside a Tokai Hit on-stage incubator set at 37°C and 5% CO2. Imaris (9.0.1) was used to reconstruct the 3D structures of monocyte-infiltrated organoids. Red signals within the size range of 12 to 22 μm in diameter (monocytes diameter) were counted as infiltrated monocytes. For each condition, five organoids were imaged. Quantification was performed on the basis of the 3D reconstruction of organoids using ImageJ (version 1.54f).

Preparation of Aβ aggregates

Aβ (1–42) peptides (AnaSpec) were resuspended in dimethylsulfoxide to 10 mM and sonicated for 10 min to enhance solubility. Aβ peptides were diluted to 1 mM in 1× PBS and stored at −80°C. Immediately before use, Aβ peptides were diluted to the final concentration used in experiments.

Imaging and quantification of Aβ phagocytosis

The hCO-MPSs were loaded with HyLite 488 Aβ (1–42) (AnaSpec) with overnight rocking. The Vybrant Dil (Invitrogen)–labeled monocytes were cocultured with Aβ-treated organoids for 24 hours before visualization. A Leica SP8 confocal microscope was used to capture monocytes across multiple planes using z-stacks. We systematically chose representative areas across all treatment conditions to ensure objective selection of regions of interest, specifically targeting the top, middle, and bottom z-positions. Red signals within the size range of 12 to 22 μm in diameter (monocytes diameter) were regarded as monocytes. For each hCO, three regions were selected for quantification, with five hCOs analyzed per condition. Imaris (9.0.1) was then used to reconstruct the 3D structures of monocytes. Aβ aggregates that overlap with monocytes in all three dimensions were counted as Aβ phagocytosis by monocytes and the fluorescence intensity was quantified using ImageJ (version 1.54f).

Sample preparation for ScRNA-seq

Forty-eight hours after monocyte coculture with hCO-MPSs, AD and AC hCO-MPSs were dissociated into a single-cell suspension using a papain-based dissociation kit (Neural Tissue Dissociation Kit-P, Miltenyi Biotec), with slight modifications to the manufacturer’s protocol. Specifically, organoids were washed three times with HBSS without Ca2+ and Mg2+ and cut into hemispheres using scissors. The hemispheres were incubated in enzyme mix 1 (enzyme P) with orbital shaking at 90 rpm for 15 min. Subsequently, enzyme mix 2 (enzyme A) was added, and the incubation continued for an additional 30 min with periodic trituration every 10 to 15 min using a wide-bore 1-ml pipette tip. After incubation, the samples were filtered through a 30-μm strainer (Miltenyi Biotec) to remove large aggregates and retain single cells. The resulting single-cell suspension was centrifuged at 300g for 8 min at 4°C, resuspended in organoid medium without growth factors to a concentration of 1 × 106 cells/ml, and subjected to scRNA-seq (10x Genomics).

scRNA-seq data analysis

Raw sequencing data were preprocessed using the Cell Ranger software on the 10x Genomics cloud platform (version 6.1.2). Reads were aligned and quantified using the hg38 human reference genome (refdata-gex-GRCh38-2020-A, 10x Genomics). Downstream data analysis was performed in R (version 4.3.2) using the Seurat package (version 4.4.0) (82). Genes expressed in less than 10 cells were excluded to remove low quality genes; cells with nFeature_RNA between 200 and 7000, nCount_RNA less than 25,000, and percent.mt less than 10% were kept in the dataset for downstream analysis. The data were analyzed following a standard workflow (83). In brief, normalization and feature selection were performed for the Seurat object, and the FindIntegrationAnchors and IntegrateData Seurat functions were used to integrate the datasets. The output was then passed through ScaleData, RunPCA, FindNeighbors, and FindClusters functions in Seurat for data scaling, linear dimensional reduction, and cell clustering. Uniform manifold approximation and projection (UMAP) was applied for data visualization. The cells projected on the UMAP space were annotated using canonical marker genes for neurons, progenitors, intermediate progenitors, astrocytes, other glial cells, and monocytes (17, 84, 85). The cluster “others” refers to cells within the organoid that cannot be definitively classified as a known neuronal or glial cell type based on their gene expression profiles. DEGs between cell clusters and groups were identified using the FindMarkers function in Seurat. Only DEGs with a P value of <0.05 were included in the analysis.

Sample preparation for bulk RNA-seq

RNA was extracted from doughnut-shape hCOs and conventional spherical hCOs using the Quick RNA MicroPrep Kit (Zymo) following the manufacturers’ protocols. The extracted RNA was then sent to Novogene for bulk RNA sequencing.

Bulk RNA-seq data analysis

The bulk RNA-seq data were analyzed and visualized using BioJupies analysis platform according to the instructions (https://maayanlab.cloud/biojupies/) (86).

GO enrichment analysis

DEGs with a P value of <0.05 were imported into the Enricher analysis platform (https://maayanlab.cloud/Enrichr/) for GO analysis according to the instructions (87). Only GO terms with a P value of <0.05 were included in the analysis.

TUNEL staining

TUNEL staining was performed on cryo-sectioned organoids to visualize cell apoptosis using the In Situ Cell Death Detection Kit (Roche) according to the manufacturer’s protocol. In brief, the slides were first fixed in fixation solution (4% paraformaldehyde in 1× PBS) at room temperature for 20 min followed by washing with 1× PBS for 30 min. Subsequently, the slides were incubated with the permeabilization solution (0.1% Triton X-100 and 0.1% sodium citrate in 1× PBS) on ice for 2 min. The slides were then incubated with the TUNEL reaction mixture at 37°C in a dark humidified box for 1 hour. Last, the slides were mounted with the antifade mounting medium. Imaging was performed using a confocal microscope (Leica Stellaris 8).

Imaging and quantification of astrocyte activation and neuronal apoptosis

Astrocyte activation was quantified using immunofluorescence slides stained with an anti-GFAP antibody (table S2). Neuronal apoptosis was assessed through slides stained with anti-NeuN and TUNEL assay. For each organoid, one representative slide was selected for quantification. Imaging was performed using an inverted fluorescence microscope (Olympus IX-83), and fluorescence intensity and cell ratios were analyzed using ImageJ (version 1.54f). Data from each slide were reported as a single data point in the results.

ELISA assay

The ELISA was used to measure cytokine concentrations in the cell culture supernatant. Forty-eight hours after monocyte coculture with hCO-MPSs, 250 μl of supernatant was aspirated with a pipette and centrifuged at 300g for 10 min to remove cell debris. Two hundred microliters of the supernatant was then diluted 3 and 10 times for the measurement of IL-1β and CCL3 concentrations, respectively. Human IL-1 beta ELISA Kit and Human MIP-1α (CCL3) ELISA Kit (Invitrogen) were used following the manufacturer’s protocol. For each ELISA assay test, 50 μl of the diluted supernatant was used, and the absorption reading was performed on the Synergy H1 plate reader (BioTek).

Reverse transcription qPCR analysis

To analyze gene expression profiles of hCO-MPSs, the organoids were first washed twice with 1× PBS, and the RNA was extracted using the Quick RNA MicroPrep Kit (Zymo). The extracted RNA was immediately reversed transcribed into complementary DNA (cDNA) using the qScript cDNA synthesis kit (Quantabio). The resulting cDNA was then subjected to quantitative polymerase chain reaction (qPCR) analysis using the SYBR Green real-time PCR master mix (Thermo Fisher Scientific). Detailed primer sequences used for qPCR are listed in table S3. Data were analyzed using the ΔΔCt method. Gene expressions were normalized to the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase. Each qPCR reaction was performed in triplicate.

ROS measurement

Measurement of ROS produced in hCO-MPSs was as follows: ROS levels were measured using the Total ROS Assay Kit 520 nm (Life Technologies) following the manufacturer’s instructions. In brief, organoids within the MPS were incubated with the ROS Assay Stain solution for 1 hour. The staining medium was then removed and replaced with fresh culture medium. AD or AC monocytes (1 × 105 cells per organoid) were subsequently added to the hCO-MPSs. After 48 hours of coculture, ROS levels were assessed using an inverted fluorescence microscope (Olympus IX-83). Fluorescence intensity was quantified using ImageJ software (version 1.54f).

Measurement of ROS produced by monocytes was as follows: Monocytes were labeled with the Vybrant DiO dye (Invitrogen) diluted 1:1000 in RPMI-1640 for 1 hour. Monocytes were then loaded onto the organoid device or cultured with Aβ (1–42) (AnaSpec). The ROS production was then detected with the CellROX reagent (Gibco) that was added into the coculture system at 5 μM for 30 min. The cells were then immediately fixed with 10% formalin and washed twice with 1× Dulbecco’s PBS (Gibco). The ROS dye was visualized under an Olympus IX-83 inverted fluorescent microscope, and the data analysis was performed using ImageJ (version 1.54f).

Neutralization of IL-1β and CCL3

To fully neutralize the target cytokines, human IL-1β neutralizing antibody (1 μg/ml; InvivoGen) and human CCL3 neutralizing antibody (2 μg/ml; BioLegend) were added on the basis of the manufacturer’s recommendations and the cytokine concentrations present in the system. The antibodies were preincubated with the organoids for 30 min at 37°C to ensure effective binding and enhance neutralization efficiency. Following preincubation, 100,000 AD-derived monocytes were introduced into each well. After 48 hours of coculture, organoids were collected for downstream analyses.

Statistical analysis

The Kolmogorov-Smirnov test was used to assess normality, while the F test was used to compare variances. Unpaired t tests were used for the statistical comparison of experimental groups. The statistical significance of differences in values is denoted as: *P < 0.05, **P < 0.01, **P < 0.005, and ****P < 0.001. All statistical analyses were performed in GraphPad Prism 8.

Acknowledgments

We acknowledge Z. Li and N. Wang for the help on data analysis and sample preparation. We thank the Indiana University Imaging Center for providing the instruments.

Funding: F.G. acknowledges the support from the National Institute of Health Awards (U54AG090792, DP2AI160242, and U01DA056242) and the National Science Foundation Award (EFRI2422149). Y.S. acknowledges the support from the National Institute of Health Award (R01AG072291). J.C. is a predoctoral scholar in the Stem Cell Biology and Regenerative Medicine Research Training Program of the California Institute for Regenerative Medicine (CIRM).

Author contributions: Conceptualization: F.G., C.T., Z.A., J.K., and T.K. Resources: F.G., Z.A., L.C., and M.G. Methodology: F.G., C.T., Z.A., M.G., H.C., and H.N. Investigation: C.T. and Z.A. Visualization: F.G., C.T., and Z.A. Supervision: F.G., Z.A., and Y.S. Writing–original draft: F.G., C.T., and T.K. Writing–review and editing: F.G., C.T., Z.A., J.C., Y.S., M.G., J.K., H.C., H.N., and T.K. Funding acquisition: F.G. Validation: C.T., Z.A., L.C., H.C., and K.T. Formal analysis: F.G., C.T., Z.A., and K.T. Data curation: F.G. and Z.A. Software: F.G. Project administration: F.G.

Competing interests: The authors declare that they have no competing interest.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. This study did not generate any new code. The code used for scRNA-seq data analysis can be obtained from Seurat website (https://satijalab.org/seurat/articles/pbmc3k_tutorial).

Supplementary Materials

The PDF file includes:

Figs. S1 to S10

Tables S1 to S3

Legends for movies S1 and S2

sciadv.adu2708_sm.pdf (2.2MB, pdf)

Other Supplementary Material for this manuscript includes the following:

Movies S1 and S2

sciadv.adu2708_movies_s1_and_s2.zip (10.1MB, zip)

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Associated Data

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Supplementary Materials

Figs. S1 to S10

Tables S1 to S3

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sciadv.adu2708_sm.pdf (2.2MB, pdf)

Movies S1 and S2

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