A vascularized liver microphysiological system captures key features of hepatic insulin resistance and monocyte infiltration

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: major_revision

Abstract
In vitro models can recapitulate aspects of human liver diseases, thereby aiding therapeutic development. Dynamic interactions with vascular and immune cells contribute to disease progression in ways that are challenging to capture in the hepatic spheroid models commonly used for assessing facets of metabolism and disease. To address this, we developed a microphysiological system (MPS) featuring multicellular human hepatic spheroids physically integrated with self-organized microvascular networks. We demonstrate this MPS’s utility by modeling an insulin resistance state, where chronic exposure to disease-mimetic conditions yields altered hepatocyte metabolism, dysregulated vascular features, and increased inflammation state. We extend this system to capture disease-relevant changes in immune cell recruitment, showing that monocytes perfused through the vasculature will extravasate toward hepatic spheroids, with insulin-resistant samples exhibiting greater infiltration. Altogether, this vascularized liver MPS captures local hepatocyte-immune-microvascular interactions in an accessible microfluidic platform, enabling the study of clinically relevant immune-tissue interactions in complex metabolic disease.

Introduction
The liver is responsible for a diverse set of vital functions, spanning plasma protein production, nutrient metabolism, immune regulation, and drug and toxin clearance1,2. This versatility is enabled by both the presence of tissue-specific cell types as well as their complex architectural organization, with hepatocytes acting as the primary arbiter of many of the organ’s key activities. In carbohydrate metabolism, the liver helps to preserve blood sugar homeostasis by maintaining a balance between the storage and secretion of glucose. Additionally, the liver is a central regulator of lipid metabolism, with hepatocytes responsible for breaking down free fatty acids into triglycerides for storage1,3. Given this organ’s essential involvement in mediating such metabolic regulatory functions, dysregulation induces systemic changes that culminate in metabolic disorders such as type 2 diabetes (T2D) and metabolic-associated steatotic liver disease (MASLD)4.

A fundamental element of these diseases is insulin resistance, defined as the body’s inability to respond effectively to the peptide hormone insulin, which plays a key role in regulating energy storage5. In the healthy liver, insulin directly induces glucose uptake and glycogen synthesis, upregulation of lipogenic gene expression, and downregulation of gluconeogenic gene expression. In contrast, insulin resistance results in impaired insulin clearance, aberrant hepatic glucose uptake and production, and elevated lipid synthesis6,7. Importantly, insulin resistance is associated with activation of liver-resident macrophages (Kupffer cells) and elevated pro-inflammatory cytokines such as IL-6 and TNF-α; this immune system response is considered part of a cycle via which inflammation can further drive insulin resistance and recruitment of circulating immune cells, which in turn causes chronic and systemic immune dysregulation8,9.

Much remains to be understood about the mechanisms by which insulin signaling in the liver is altered, as well as the complex interactions between hepatic and systemic insulin resistance leading to disease progression. These knowledge gaps are in part due to a historical reliance on animal models, which often exhibit significant disparities from human phenotypes and establish pathophysiology through dissimilar mechanisms10,11. As a result, translation of most findings to the human context has seen limited clinical success. To date, resmetirom is the only U.S. FDA-approved therapeutic for adults with metabolic dysfunction-associated steatohepatitis (MASH), an advanced stage of MASLD, exemplifying the ongoing gap in our understanding of metabolic liver disease12.

In recent years, microphysiological systems (MPS) have emerged as a promising alternative to animal models, presenting new opportunities for advancing our understanding of human biology and disease13,14. By enabling 3D co-culture of multiple cell types within meso- or microfluidic devices, MPS can maintain primary human liver phenotypes longer and more faithfully than culture in 2D14–17. Further, efforts to incorporate tissue-specific non-parenchymal cells (NPCs)—including Kupffer cells, stellate cells, and liver sinusoidal endothelial cells (LSECs)—capture key functional contributions and cellular interactions that are otherwise lost in simpler culture contexts8,18,19. Recent studies demonstrating improved primary human hepatocyte longevity and function with either synthetic vascular-like channel structures or co-culture with endothelial cells underscore the value of adding vascular elements20–22. In addition, accessible microfluidic models with 3D perfusable vasculature have been employed to study immune cell trafficking for some applications23–26, but they have not yet been used to enable direct transport of immune populations to hepatocytes themselves in situ, in part due to challenges with integrating perfused vasculature within hepatic tissue structures27–30.

In parallel, liver MPS have been applied to a variety of disease states, including MASLD/MASH. These models employ tailored culture media compositions (e.g., high nutrient concentrations) designed to induce specific phenotypes (e.g., features of hepatocyte steatosis)31–35. While inclusion of NPCs within these disease models has improved their fidelity to disease phenotypes, the known roles of vasculature and immune cell trafficking in disease pathogenesis motivate our building more accessible models that incorporate these elements36–39.

Here, we establish a primary human cell-based, vascularized disease model of hepatic insulin resistance. By leveraging and improving upon self-organized in vitro vascular models, we developed a microfluidic platform where vasculature perfuses directly into multicellular hepatic spheroids comprising hepatocytes, Kupffer cells, endothelial cells, and fibroblasts, exhibiting high degrees of interaction and enabling transport directly into spheroid structures. Beyond supporting hepatocyte and vascular function, this MPS also captures features of disease phenotypes. A pathological disease media formulation induces an insulin resistant state in this vascularized liver platform, with altered insulin clearance and inflammation profiles. Finally, we demonstrate that these altered inflammation states functionally impact the relationship between the local environment and systemically circulating innate immune cells. With the addition of peripheral monocytes, we show enhanced monocyte recruitment under insulin resistance conditions, recapitulating immune-tissue interactions in metabolic disease.

Results
Multicellular hepatic spheroids integrate with self-organized vascular networks
We first generated liver spheroids using an alginate microwell system40. These spheroids comprised donor-matched primary human hepatocytes and Kupffer cells; human umbilical vein endothelial cells (HUVECs); and normal human lung fibroblasts (NHLFs). The formation of perfusable microvascular networks in fibrin hydrogels via morphogenesis of HUVECs and NHLFs is a well-established protocol in the MPS community. We speculated that also including these two vascular elements in our hepatic spheroids would assist in establishing vascular junctions between the spheroids and their surrounding microvasculature41,42. To create a suitable microfluidic platform, we adapted a polydimethylsiloxane (PDMS) device design previously used for vascularization studies43–46 by bonding devices to 6-well glass bottom plates, facilitating higher throughput culture and improved long-term time-lapse imaging (Supplementary Fig. 1). After these spheroids compacted for 2 days, we harvested them, mixed them with fibrin gel precursor containing additional HUVECs and NHLFs, and introduced this mixture into a microfluidic device. After fibrin polymerization, we maintained the cultures with daily media changes for 7-14 days, using a rocker platform to promote vasculogenesis and longer-term maintenance of perfusable microvessels (Fig. 1A).

To enable co-culture of these primary human cell types, we first defined a media supportive of liver and vascular function, inspired by combining commercial formulations for hepatocyte and endothelial cell media, while maintaining physiological ranges of glucose, insulin, and free fatty acid levels47 (Supplementary Table 1). Hepatic spheroid structural maintenance and microvascular network formation and perfusability were both independently assessed in this new media formulation over 1-2 weeks in culture (Supplementary Fig. 2).

Live imaging demonstrated that HUVECs self-organize into microvascular networks spanning the device tissue compartment over the first 3-5 days in culture (Supplementary Fig. 3, and Supplementary Movie 1). The microvascular networks surround the hepatic spheroids, establishing the spheroids as both interacting with and integrated within themicrovasculature (Fig. 1B). To test whether these interactions were in part due to the endothelial cells within the hepatic spheroids forming links with those external to the spheroid, we generated hepatic spheroids containing RFP-HUVECs and monitored their interactions with surrounding GFP-HUVEC vascular networks. We observed that endothelial cells that originated within the spheroids not only connected the spheroids to the immediately adjacent GFP-HUVEC microvessels, but also migrated to distant locations throughout the networks after several days in culture (Fig. 1C, and Supplementary Fig. 3). Additionally, we confirmed that after 14 days of device culture, hepatocytes and Kupffer cells remain integrated with the networks (Fig. 1D).

Beyond visualizing these physical interactions in co-culture, we also evaluated critical structural and metabolic functions of the vasculature and hepatocytes. We established that the microvascular networks had open lumens by perfusing fluorescent dextran through each device and observing the dye localization within vessel structures (Fig. 2A). Notably, perfusable segments form through the hepatic spheroids (Fig. 2B, and Supplementary Movie 2) and span the entirety of the central hydrogel compartment (Supplementary Fig. 4). By having vasculature that perfuses directly through hepatic spheroids and thus enables transport into the tissue structures, this system improves upon previous vascularized liver models, which often show vasculature adjacent to liver spheroids rather than in an integrated culture format27,29.

Hepatocellular function was assessed in both physiological baseline medium and a metabolic overload “insulin resistance”-inducing (IR) media formulation featuring high insulin, glucose, and free fatty acid levels (Supplementary Table 2). Hepatocytes in both media formulations sustained albumin production throughout the course of the experiment, with a drift upward in magnitude over 2 weeks in culture, an improvement over hepatic spheroids alone (Fig. 2C, and Supplementary Fig. 5). Further, hepatocytes exhibit robust CYP3A4 activity and urea production after 2 weeks of culture in MPS devices, with comparable values detected in both media formulations (Fig. 2D, E). We confirmed that this MPS model is generalizable to additional primary liver cell donors (Supplementary Fig. 6, and Supplementary Table 3). With these benchmarks of hepatic function meeting ranges in accordance with in vivo function48, we then investigated additional behaviors of the vascularized liver MPS in simulated disease conditions.

Vascularized liver platform recapitulates known features of insulin resistance disease state
We specifically sought to understand whether this vascularized liver MPS can be applied to model features of hepatic insulin resistance that contribute to liver and systemic metabolic disorders. We hypothesized that culturing our devices in IR media would induce phenotypes associated with an insulin resistance disease state. We began by investigating the clearance of insulin by the cells in our system, a process known to be dysregulated in hepatic insulin resistance7. While both the physiological and IR media conditions initially showed equal insulin clearance rates, sustained exposure to the IR media resulted in a decline in clearance that is consistent with insulin resistance pathogenesis (Fig. 3A). This trend was recapitulated in additional primary liver cell donors as well (Supplementary Fig. 7).

Additionally, insulin signaling partially regulates glucose production by hepatocytes. When comparing the IR condition to the physiological condition, gene expression analysis by qPCR revealed significantly elevated levels of genes involved in gluconeogenesis (PCK1 and G6PC) and smaller changes detected in genes involved in fatty acid transport and synthesis (FASN and FABP1) (Fig. 3B). To establish whether this altered gene expression was associated with observable functional changes, we adapted a canonical assay to measure hepatocellular glucose output in a vascularized system, in which media is changed to a reduced glucose production media containing pyruvate and lactate (see Methods) for 24h49–51. The IR condition resulted in higher net glucose production compared to the physiological condition, indicating impaired metabolic regulation (Fig. 3C). We note that with this 24h timescale, the absolute magnitude of glucose output observed in device culture is lower than in microwell culture of free hepatic spheroids (Supplementary Fig. 5E)—highlighting our MPS model’s ability to capture the physiologically relevant balance of glucose production by hepatocytes and consumption by vascular cells. To more specifically measure differences in glucose production, we also performed a shorter version of the assay and confirmed that net glucose levels in our vascularized system align closely with those in our spheroid-only benchmark condition after 2h of culture in glucose production media (Supplementary Fig. 8). Notably, the greater net glucose levels in the IR condition—even after insulin stimulation—are consistent with the observed metabolic gene expression changes (Supplementary Fig. 8).

Due to the association between chronic inflammation and insulin resistance, we also analyzed a panel of chemokines, cytokines, and growth factors associated with inflammation state or vascular dysregulation. This panel was assayed via Luminex on supernatant collected on Day 10 of device culture (Fig. 3D, and Supplementary Fig. 9). To better identify key features associated with the IR state, we performed supervised multivariate analysis to identify features associated with each media condition using partial least squares discriminant analysis (PLS-DA). This model was able to significantly separate samples by physiological and IR conditions (Fig. 3E). Examining the PLS-DA loadings driving this separation, we see chemokines such as CXCL1, CXCL2, and CCL4 correlating with IR conditions (Fig. 3F). This aligns with clinical observations of increased hepatic infiltration by immune cells in IR and MASLD, with this acute inflammation further driving chronic insulin resistance52,53.

Interestingly, from our Luminex panel we also saw increased secretion of the glycoprotein ICAM-1 associated with the IR devices, suggesting a pro-inflammatory endothelial cell disease state. Some patient cohort studies have suggested that elevated plasma levels of ICAM-1 and other cell adhesion molecules could be a predictor for people at increased risk of developing T2D and associated cardiovascular complications54,55. To parse the extent to which the vascular networks themselves contribute to the disease phenotypes observed, we also performed experiments separating the two main components of our model, spheroids and microvasculature. Hepatic spheroids alone in physiological and IR conditions confirmed vasculature was not required for disease state development (Supplementary Fig. 5). Meanwhile, microfluidic culture of vascular networks alone demonstrated vascular cells contribute to some metrics, such as insulin clearance (Supplementary Fig. 10A) and inflammation profiles (Supplementary Fig. 10C-E), but features such as gluconeogenic gene expression (Supplementary Fig. 10B) did not change in the absence of liver cells, as expected.

While we initially focused on hepatocyte-specific metrics in our comparisons of the physiological vs. IR media conditions, we also noticed that IR media led to the development of consistently narrower vascular networks in our liver MPS during initial dextran perfusion tests (Fig. 4A). This observation, in combination with our PLS-DA findings of a potentially inflamed endothelial state, motivated us to further quantify macro-scale differences in vascular morphology and permeability (Fig. 4B). Imaging our vascularized liver MPS, followed by quantification of vessel features56, showed that by Day 6, there was significantly reduced vessel coverage under IR conditions compared to the physiological baseline (Fig. 4C), as well as narrower vessels (Fig. 4D). In parallel, our permeability analysis showed that the narrowed, IR-associated microvascular networks also display increased permeability, resulting in “leakier” vessel structures (Fig. 4E). Interestingly, these results not only mimic reported effects of high glucose on 2D endothelial cultures, but also reflect microvascular complications often seen in diabetic patients, with increased capillary permeability and narrower vessels being hallmarks of diabetic microangiopathy and peripheral vascular disease57–60.

Establishing peripheral immune-liver interactions
In addition to peripheral vascular dysregulation, insulin resistance and MASLD are also clinically associated with chronic inflammation and increased immune cell recruitment to the liver52,53. These features, in combination with our earlier results demonstrating elevated chemokine and cytokine levels in the IR liver MPS, motivated us to investigate immune cell recruitment to hepatic spheroids as a function of induced disease state.

For these experiments, we generated the vascularized liver MPS as previously described in either physiological or IR conditions. After one week, we added CD14+ monocytes freshly isolated from healthy human donors to the microvasculature and maintained this immune co-culture under static conditions for an additional 4 days (Fig. 5A). Time-lapse imaging of CellTracker-labeled monocytes showed these cells immediately travel through the vascular networks (Fig. 5B). While some quickly flowed to the other side of the devices, others attached to the microvascular networks, extravasated through vessel walls, or localized to hepatic spheroids within the first 12h (Fig. 5C, and Supplementary Movie 3). After 4 days, we saw these monocytes persist within the vascularized liver MPS, with many remaining closely associated with the hepatic spheroids. Furthermore, a subset of these monocytes appeared to begin differentiating based on their reduced circularity and cell spreading, indicating longer term colonization of the hepatic niche (Fig. 5D).

To discern if there were significant differences under IR conditions, we performed flow cytometry on these samples to quantify monocyte frequency. To do so, we manually cut out the central compartment from each device, digested the hydrogel to isolate single cells, and ran flow cytometry to measure the fraction of CD14+ cells residing within the tissue compartment of each device (Fig. 5A). Gating for the live, CellTracker-positive population in each device allowed for identifying changes in monocyte frequency (Supplementary Fig. 11, and Fig. 5E). We saw significantly increased monocyte frequency in the IR liver MPS samples. To further understand the role of hepatic spheroids in driving this immune cell recruitment, we also cultured monocytes in devices that contained only vascular networks. Comparisons between our full liver MPS model and this vascular networks-only control revealed the hepatic spheroids were responsible for significantly elevated monocyte recruitment (Fig. 5F). Cell surface marker characterization revealed monocytes cultured in these devices display elevated CD163 compared to the initial monocytes, indicating differentiation towards CD163+ tissue macrophages (Fig. 5G). Previous studies have examined soluble CD163 as a marker of insulin resistance61,62. Future work characterizing the effects of the recruited CD163+ macrophages in our system, including activation and shedding of CD163, could be an interesting avenue for further investigation. Additionally, comparisons between liver MPS samples with vs. without monocytes show that the addition of monocytes leads to elevated CCL2 and vasculature-associated soluble cues (Supplementary Fig. 12). Ultimately, these altered frequencies and inflammation state between conditions and monocyte differentiation highlight the ability to examine altered recruitment and immune-tissue interactions in this MPS.

Methods
Ethical statement
This study was conducted following all relevant ethical regulations and guidelines. The primary human liver cells used in this study were sourced from LifeNet Health (Virginia Beach, VA USA). Informed donor consent and research permissions were obtained by LifeNet Health for all donor cells used in this study. Primary human monocytes used in this study were sourced from buffy coats from deidentified healthy donors via the Massachusetts General Hospital Blood Transfusion Service with approval from the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (E-4269).

Cell culture
Human umbilical vein endothelial cells (HUVECs; Angioproteomie, cAP-0001) and normal human lung fibroblasts (NHLFs; Lonza, CC-2512) were cultured in flasks using VascuLife VEGF Endothelial Medium Complete Kit (VascuLife; LifeLine, LL-0003) and FibroLife S2 Fibroblast Medium Complete Kit (FibroLife; Lifeline, LL-0011), respectively. GFP- and RFP-expressing HUVECs were used in studies that involved real-time visualization of microvascular network formation (Angioproteomie, cAP-0001GFP and cAP-0001RFP). Cells were cultured at 37°C and 5% CO2 in a humidified incubator, with media changed every other day. HUVECs and NHLFs were detached using TrypLE (ThermoFisher) and used between passages 5-9 for all experiments.

Primary human hepatocytes and Kupffer cells in this study were donor-matched from three individuals (Supplementary Table 3) (LifeNet Health Life Sciences). Experiments presented in the main text were all performed with hepatocytes and Kupffer cells from Donor #1 (53-year-old Hispanic male). Hepatocytes were thawed in Cryopreserved Hepatocyte Recovery Media (CHRM; ThermoFisher), spun down at 100g for 8 minutes, and resuspended in our custom “physiological” William’s E/Vasculife media formulation (Supplementary Table 1). Kupffer cells were thawed in ice cold William’s E media and spun at 450g for 5 minutes before being resuspended in physiological medium. Both hepatocytes and Kupffer cells were thawed directly before spheroid formation.

Multicellular hepatic spheroid formation
Spheroids were formed using alginate microwells fabricated using a PDMS reverse-mold as previously published40. Alginate microwells were primed with 5% BSA for at least 2h at 37°C before seeding. 120,000 primary human hepatocytes, 60,000 HUVECs, 12,000 Kupffer cells, and 12,000 NHLFs were pre-mixed to a volume of 75 µL per well. After removing the BSA solution, alginate microwells were primed with 75 µL of culture media, then the spheroid cell mixture was added dropwise directly onto the alginate microwells. In total, each alginate well contains 600 spheroids, where each spheroid is composed of ~200 hepatocytes, 100 HUVECs, 20 Kupffer cells, and 20 NHLFs (with some inherent level of variability).

Microfluidic device fabrication
The microfluidic devices used in this study consist of three parallel microchannels and were fabricated using soft lithography as previously described44. Briefly, polydimethylsiloxane (PDMS; Sylgard 184, Ellsworth Adhesives) elastomer and curing agent were mixed in a 10:1 w/w ratio, degassed and poured onto a silicon master mold, and cured at 60°C overnight. The PDMS was removed from the mold and cut into individual devices, and biopsy punches were used to create inlet/outlet ports in the central gel channel (2 mm) and media reservoirs (5 mm). Dust was removed from the PDMS surfaces using Scotch tape before the devices were dry sterilized in the autoclave. The PDMS devices were then treated with plasma (Harrick Plasma Cleaner) for 2 minutes and bonded to 6-well glass-bottom plates (Cellvis, P06-1.5H-N) to facilitate efficient live imaging during experiments (Supplementary Fig. 1). After plasma bonding, devices were left overnight in a 60°C oven to recover hydrophobicity before use. For this study, a device geometry was selected in which the central gel channel measures 3 mm wide and 500 µm tall, in order to provide sufficient space for microvascular networks to form around the ~150 µm spheroids43. For experiments aimed at testing a higher ratio of spheroids to vascular network cells, a smaller microfluidic device geometry was used, with a central gel channel that measures 1.8 mm wide and 400 µm tall and accommodates a total of 10 µL of gel. For initial media screens to test perfusable network formation (without spheroids), AIM Biotech idenTx chips were used with the same cell concentrations.

Device seeding
After 48 hours of aggregation in alginate wells, spheroids were harvested from alginate wells by pipetting with a wide bore pipette tip and incubated with AlgiMatrix Dissolving Buffer (Gibco, A11340) for 5 minutes before spinning down at 50g for 2 minutes. Supernatant was aspirated and cells were resuspended in 1 mL of media. After spheroids naturally settled by gravity, media was aspirated to remove loose single cells and spheroids were resuspended again before being combined with HUVECs and NHLFs.

Fibrinogen from bovine plasma (Sigma, F8630) was dissolved at 37°C in Dulbecco’s Phosphate-Buffered Saline (DPBS) to a concentration of 6 mg/mL. Thrombin from bovine plasma (Sigma, T4648) was reconstituted to 100 U/mL in 0.1% w/v bovine serum albumin in water, and then diluted in physiological media to a concentration of 3-4 U/mL. HUVECs, NHLFs, and spheroids were combined in a master mix and centrifuged at 200g for 5 minutes, with the resulting cell pellet resuspended in the diluted thrombin solution. For each device, 15 µL of the thrombin cell suspension was mixed thoroughly with 15 µL fibrinogen (final concentration: 3 mg/mL) and pipetted into the central gel channel using a wide bore pipette tip. The final concentrations of HUVECs, NHLFs, and spheroids were 7 M/mL, 1.5 M/mL, and ~200 spheroids/device, respectively. Some devices included only HUVECs and NHLFs as a “vascular networks alone” control. After seeding, devices were moved to a humidified incubator for 15 minutes to allow the fibrin gel to polymerize, after which cell culture medium (either the “physiological” or “insulin resistance” (IR) formulation, as described in Supplementary Tables 1 and 2) was added to the side channels and reservoirs. To provide interstitial flow to aid with vasculogenesis, devices were placed on an OrganoFlow rocker platform (Mimetas) set to tilt 8° every 8 minutes along the axis perpendicular to the gel channel during the experiment.

Device culture
Media changes (150 µL removed from and added to the reservoirs) were performed every 24h. Collected media was cleared of cell debris by spinning at 1000g for 5 minutes immediately after collection, then the supernatant was frozen and stored at -80°C until further use. Four days after initial device seeding, a monolayer of HUVECs was added (1 million/mL, 30 µL per side channel) as described previously, in order to facilitate the generation of perfusable microvascular networks and to prevent non-luminal transport of dye or immune cells between the central gel compartment and media channels in downstream assays44.

Multicellular hepatic spheroid culture
For some experiments, multicellular hepatic spheroids were maintained in alginate wells as a spheroid-only control for our vascularized MPS model. After 48 h of aggregation, spheroids were switched into either the physiological or IR medium formulation and maintained for 12 additional days. Media changes (300 µL per well) were performed every 48h according to previously published protocols40.

Albumin quantification
To assess hepatocyte spheroid function, stored cell supernatant was thawed at 4°C and albumin secretion was assayed via ELISA (Bethyl Laboratories, E80-129) according to manufacturer instructions.

CYP3A4 assay
Devices were rinsed with PBS before adding media containing Luciferin-IPA reagent at a 1:1000 dilution from CYP3A4 P450-Glo™ kit (Promega). Devices were incubated for 1h before media collection and stored at -80°C until downstream analysis. Media was thawed at room temperature and the assay was performed according to manufacturer protocol alongside a luciferin standard curve to calculate CYP3A4 levels. Plates were read with a Spectramax i3x plate reader luminescence cartridge.

Insulin clearance quantification
Insulin clearance was evaluated from cell supernatant via ELISA (R&D, DY8056). Physiological media samples were diluted 1:2, while IR media samples were diluted between 1:3-1:5 for each insulin ELISA. Clearance fraction was calculated by dividing the measured insulin concentration in cultured media by the measured insulin concentration in cell-free control wells.

Glucose output assay
To assess glucose production, devices were rinsed 3 times for 10 minutes in William’s E media containing no serum, glucose, or insulin. Next, devices were transitioned to a “Glucose production media” containing 50 µM glucose, 20 mM lactate, and 1 mM pyruvate for 24h (William’s E basal medium, 3.5% dialyzed Heat Inactivated FBS, 2.5 ng/mL VEGF, 2.5 ng/mL EGF, 2.5 ng/mL FGF-b, 7.5 ng/mL IGF-1, 2 mM Glutamax, 1% Pen/Strep, 100 nM hydrocortisone, 7.5 mM HEPES, 50 µm glucose, 20 mM lactate, and 1 mM pyruvate). Collected media was spun down at 1000g for 5 minutes. Glucose concentration was then measured with the Amplex™ Red Glucose Assay kit (A22189, Invitrogen).

Luminex assay
Supernatant was analyzed according to manufacturer instructions using R&D Systems Custom 28-plex or 12-plex analyte panels. Assay kits were adapted to fit a 384 well format, using 12.5 uL of sample and antibody beads per well. Samples were run at both low (2X) and high (25-30X) dilutions in duplicate on a Bio-Plex 3D suspension array system (Bio-Rad). Downstream analysis was performed in MATLAB to fit a 5-parameter logistic standard curve. Any samples with bead count less than 30 or analytes outside the limits of detection were excluded from downstream analysis. Heatmaps were generated on row z-scores using gplots in R (4.0.3). Significance was assessed via Wilcoxon rank-sum test with Benjamini-Hochberg p-value adjustment for multiple hypothesis correction. Partial least squares discriminant analysis (PLS-DA) was performed using ropls (1.20.0) and an adaptation of the systemsseRology package with LASSO feature selection on the dataset z-scores. PLS-DA scores and loadings were plotted with ggplot2 (3.3.3) and marginal distribution was plotted with ggExtra (0.10.1).

Gel harvesting
The devices were rinsed 1X with PBS, the PDMS was cut along the media channels with a sterile scalpel, the PDMS above the central gel channel was lifted, and then the gel was scooped into a tube containing 50 µL of Liberase TM (Roche, LIBTM-RO) at 0.5 mg/mL in William’s E medium. Gels were then incubated for 20 minutes at 37°C with intermittent mixing by gently pipetting up and down until the gel flowed easily and visibly broke apart. The samples were then spun down at 400g for 5 minutes. Supernatant was removed and cells were transferred to a 96-well plate for staining or frozen in appropriate reagent for downstream RNA or protein extraction.

RNA analysis
Harvested cells were frozen in Trizol at -80°C until ready for RNA isolation. Samples were thawed and incubated at room temperature for 5 minutes, then spun down at 1000g for 5 minutes to remove any residual debris. RNA was isolated with Direct-zol RNA MiniPrep kit (Zymo Research) according to manufacturer instructions, including the in-column DNase I treatment step. RNA was quantified using Nanodrop One and converted to cDNA using the High-Capacity RNA-to-cDNA Kit (ThermoFisher Scientific, 4387406). Next, qPCR was performed using TaqMan Fast Advanced Master Mix (Applied Biosystems, 4444557) with probes for PCK1 (Hs00159918_m1), G6PC (Hs00609178_m1), FASN (Hs01005622_m1), FABP1 (Hs00155026_m1), and endogenous control 18S (Hs03928990_g1). Samples were measured on a StepOnePlus Real-Time PCR system (Applied Biosystems). The delta-delta Ct method was used to calculate the relative fold change in gene expression using 18S as the internal reference.

Immune cell perfusion
Buffy coats were acquired from Massachusetts General Hospital. The blood was diluted 1X in PBS and layered over Lymphoprep (StemCell Technologies), then spun for 30 minutes with no brake. The PBMC layer was carefully removed, spun for 10 minutes, and resuspended in FACS buffer (PBS, 2% Heat Inactivated FBS, 2 mM EDTA) before proceeding directly to CD14+ cell isolation using positive selection beads (StemCell, 17858). CD14+ cells were labeled with CellTracker Deep Red (ThermoFisher, C34565) or CellTracker Red CMTPX (ThermoFisher, C34552) for 20 minutes. After resuspending monocytes to a concentration of 1 million/mL, cells were perfused through microfluidic devices by removing all media from the device, followed by addition of 40,000 monocytes to one media channel. Media was allowed to equilibrate, enabling flow of monocytes across the gel channel to the opposing media channel for 5 minutes, before adding an additional 100 µL of media to each media channel in static culture, with daily media changes for 2-4 more days.

Flow cytometry
Immediately after being harvested from devices, cells were rinsed 1X with PBS and spun at 200g for 5 minutes. Cells were labeled with a fixable viability dye (BioLegend, 423107; BioLegend 423105) for 15 minutes at room temperature. After washing in FACS buffer, cells were incubated with an FC block for 15 minutes before being stained for 40 minutes on ice. Cells were fixed with 4% paraformaldehyde for 15 minutes and stored in FACS buffer at 4°C until measurement on a BD FACSymphony A3 Cell Analyzer (BD Biosciences).

Vascular permeability analysis
After the microvascular networks became perfusable, vascular permeability was measured using time-lapse confocal imaging (Zeiss LSM 880) as previously published44. Briefly, all media was removed from the devices and replaced with a solution of 0.1 mg/mL 10 kDa Cascade Blue dextran (ThermoFisher, D1976) in media. Three random, non-overlapping regions of interest (ROIs) were chosen for each device; for each ROI, two z-stacks (step size = 5 µm, 10 steps, resolution = 640×640 pixels, ROI dimensions >= 600 µm x 600 µm, 10x objective) were captured 9 minutes apart. Vascular permeability values were computed after a custom FIJI macro was used to calculate morphological parameters and changes in the average fluorescence intensity in the vasculature and matrix over time, as previously described. For each device, the average measurement of the 3 ROIs was reported.

Vessel morphology analysis
Maximum intensity projection images were generated from the same ROIs of dextran-perfused devices used in the vascular permeability analysis. Quantitative analyses of microvascular network morphology were performed for these ROIs using the open-source tool REAVER to obtain metrics such as “mean segment diameter” (2D projected diameter) and “vessel area fraction”56. For each device, the average measurement of the 3 ROIs was reported. To control for the inherent inter-experiment variability that occurs during device seeding, measurements were compared between media conditions within the same experiment rather than pooled across experiments.

Immunostaining
Devices were washed 1X with DPBS and fixed in 4% paraformaldehyde for 30 minutes at room temperature on a rocker platform. After fixation, devices were washed 3X with DPBS and permeabilized with 0.5% Triton-X-100 in PBS for 2h at room temperature. Devices were then washed and incubated with blocking buffer overnight (5% donkey serum in PBS) at 4°C. Samples were then incubated with some combination of primary antibodies for CD31 at 1:100 (AF806, Bio-Techne, lot # DGX0523031), CD68 at 1:200 (14-0688-82, Invitrogen, lot # TE2570551), CD163 at 1:100 (AF1607, Bio-Techne, lot # JPZ0224011), and Arginase-1 at 1:200 (14-9779-82, Invitrogen, lot # 2265377), suspended in blocking buffer and incubated for 48h on a rocking platform at 4°C, before being washed 3 times and incubated with Alexa Fluor secondary antibodies at a 1:200 (A-21447, Invitrogen, lot # 1977345) or 1:500 dilution (A11015, Invitrogen, lot # 2785566; A10037, Invitrogen, lot # 2300930) in blocking buffer for 48h at 4°C. All fluorescent images were taken on a Zeiss LSM 880 confocal microscope (10x, 20x objectives) or a Keyence BZ-X800 fluorescence microscope (4x, 10x objectives).

Statistical analysis
Statistical analysis was performed using GraphPad Prism 10. Bar plots show averages with error bars representing standard deviation (SD). Mann-Whitney tests were performed in comparisons of two groups. Benjamini-Hochberg (BH) multiple hypothesis correction was applied for analysis of multiple comparisons in Fig. 3. For multiple groups, two-way ANOVAs with appropriate post-hoc tests were performed. Statistical significance was defined as *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study describes use of liver spheroids embedded in a self organized network of (HUVEC) endothelial cells in a microfluidic chip to mimic human hepatic insulin resistance. The flow of the paper presents the experiments in a structured and easy to follow way. Although vascularized liver chips have been reported before, this is to my knowledge the first report that is applied to mimic insulin resistance. Overall, this work is of value to the field as it shows a novel application of vascularized liver models. The story is weakened by use of non tissue specific cells, ie liver endothelial cells and stellate cells and as such is lagging compared to other vascularized liver reports. Before considering this story for publication several points should be addressed:\n1. Authors should comment on their choice to use human primary liver endothelial cells and stellates instead of HUVEC and lung fibroblasts and\n2. Authors mention that ‘Microvascular networks both surround and penetrate through the hepatic spheroids, establishing the spheroids as both interacting with and integrated within the microvasculature (Fig 1B).’ Surrounding is clearly visible in the provided pictures, is not clear however if they are penetrated or penetrating. Authors should provide more convincing evidence of this or rephrase.\n3. In relation to results from the glucose output assay: Is glucose output somehow normalized, ie per cell number, per protein or DNA content? The authors refer to these results as glucose production, yet the starting concentration of glucose in the gluconeogenic media is 50µM glucose – wouldn’t the graph in the figure 3C represent rather glucose consumption over 24h as the final concentrations are below initial input?\nIs there any glucose retained in the ECM or microfluidic channels during high gluceose exposre (IR condition) that could the low glucose exposure findings? Authors should include a cell free condition to rule this out.\nA 24h timeline for gluconeogenesis is rather long, especially for co-culture with other cell types, that rely on glycolysis.\nAuthors acknowledge in the result section that glucose production is lower than based on hepatocyte-only 3D culture. which is not in the dataset, nor has references. This part requires data or better data annotation (is it in SFig5 E?) or should be moved to the discussion section. Also, please clarify if this hepatocyte-only 3D culture is performed in the same microfluidic device.\n4. In Figure 4 Authors show that IR media condition cause reduced vessel coverage. Would this finding suggest less endothelial cells in IR conditions? If yes, is there a possibility that this higher glucose concentration in IR media condition is a result of less cells consuming glucose provided/or produced? This possibility should be addressed by the authors.\n5. Hepatic insulin resistance is usually shown through impaired suppression of hepatocyte glucose production by insulin. Ideally this feature should be shown in the device in the similar setup as in SFig5E. Is this elevated glucose output in IR conditions not sensitive to 5nM Insulin exposure?\n6. Related to qPCR data: On which day is the expression profiling performed? How many independent experiments are represented in the graph Fig3B? What is the contribution of different cell types to the overall gene expression?\nThe authors additionally show the expression profile of the vascular network without spheroids. However, this comparison alone does not allow for a direct deduction of the relative contribution of spheroids to gene expression levels. When different cell types interact, their gene expression profiles are likely to change dynamically in response to signaling cues, flow, ecm- and cell-cell communication. Authors should address it or use more appropriate approach.\nFurthermore, how would the gene expression profile change under the low glucose condition presented in Fig3C?\n7. Related to Insulin ELISA:\nAuthors show the insulin clearance fraction in relation to the cell-free controls. Is there any insulin binding to the ECM/microfluidics? If any, what is the dynamic of this binding?\nWhat is the volume collected from the device for the insulin ELISA? Is the undiluted sample measured or diluted further before ELISA? Insulin clearance in the “physiological” conditions show very small SD between the 8 replicates. If on average 80% of the initial concentration is cleared, the measured values will be around 40pM. If sample is diluted this can get the values very close to the lower limit of the assay. Is this result (low variation) a biological phenomenon or rather related to the values of ELISA getting close to the lower limit of detection? Assaying the IR condition seems to yield bigger SD. Is that related to the insulin values being measured in the ELISA assay range for this kit?\n8. Related to Luminex data. Fig 3 D shows Luminex panel for samples taken at the 10 day of culture. Although a heatmap with row normalization neatly shows the results it would be good to understand for the reader what is the actual spread of the data. This is an interesting data set and it is important to provide this detail. Providing supplementary data (e.g. in a form of table) with concentration per analyte per sample would give that full picture on what to expect from this model in terms of concentration range per analyte.\n9. Reproducibility: all results only list the technical replicate number (n), but not the biological/independent experiment number (N). Wouldn't it be beneficial to conduct at least three independent experiments for model validation? How reproducible can the data be considered?\n10. Can the authors say something about stability over time of the Kupffer cells in 14 days of device culture? They are present at day 14 but did they increase/decrease over time?\n11. Suppl. fig 3A seems to show a lower density spheroids compared to suppl figure 4. What is the distribution and variation in density of spheroids in MPS system?\nMinor comments:\n1. A method description for the spheroid-only dataset is missing .\n2. Spheroids are made from 120,000 primary human hepatocytes, 60,000 HUVECs, 12,000 Kupffer cells, and 12,000 NHLFs were pre-mixed to a volume of 75µL per well. Authors should clarify number of spheroids made and number of cells that are in these spheroids.\n3. Please, add to methods section at which day of aggregation spheroids were added to the device. Is it always day 2 (48h post aggregation)? Please, add for better clarity.\n4. Add individual data values in the graphs in Fig 3A, Fig2C\n5. Glucose Output Assay section; should be “50µM glucose” instead of “50µm glucose”\n6. Hepatocyte secretion of albumin, normalized per million hepatocytes, shows increased albumin levels over time, n = 11 devices per media condition. How was this correlated? With seeded hepatocytes? Was there variation in size and number hepatocytes in the spheroids? Spheroids-only secreted at day 3 around 5ug/day/106 cells seeded. In the chip it is after 2 days of culture already 10 times higher. Is the data of same hepatocyte donor? Do the authors have an explanation?", "reviewer_2": "This study showcases a microfluidic model of the liver containing hepatic spheroids and surrounding microvasculature. It is well written, and data is effectively presented. However, there are concerns with the work as follows.\n1. The technological novelty is minimal as authors have published on the microvasculature/microfluidic device before and hepatic spheroids have been published on quite a lot in the field. So, their combination is logical. A similar device has been published before (https://www.pnas.org/doi/10.1073/pnas.2115867119) that includes a HUVEC-lined vessel and hepatic spheroid in a gel surrounding it. While it lacks microvasculature (though there is no HUVEC microvasculature in the liver), the utility is not so dissimilar for the application being showcased.\n2. Only a single hepatocyte donor (and one Kupffer cell donor) for the studies so it is not clear if this technique is applicable to a larger set of donors as primary human hepatocytes can be quite variable in function/viability. In fact, even with this one donor, functions in the devices are variable, see for instance Supplemental Figure 5 and main Figure 2, and it is mostly likely not due to the presence of the microvasculature.\n3. The findings for hepatocyte response to the disease media are not new as even the authors have shown something similar in their publications along with others. It is well established that hepatocytes become insulin resistant and produce more glucose (and clear less insulin) in vitro when treated with high glucose/insulin/fatty acid media.\n4. The results with the microvasculature with disease stimuli are interesting but the analysis of the endo phenotype and the monocyte-to-macrophage conversion in the spheroids is a much smaller part of the paper and done fairly minimally with respect to cell phenotype analysis.\n5. The proper control for this study would have been endothelial cells on transwells and placed atop hepatic spheroids to determine if similar outcomes could have been obtained with endothelial permeability in the presence of hepatic insulin resistant state. This would allow 96-well plate studies versus the 6-well plate devices featured here.\n6. Functional analysis of the device is fairly minimal. Albumin is shown as time-series (and even that is variable across figures as mentioned above) while CYP3A4 and urea are only shown for a single time-point. Is the function stable over time and across multiple batches of the devices? This is not clear.\n7. The spheroid composition (2X hepatocytes, 1X endothelial cells, 0.2X KCs and 0.2X lung fibroblasts) is nowhere near what a liver should contain. How this composition affects the findings is not clear. The authors show how their dysfunction resembles clinical findings, but this is not surprising given the disease media affecting hepatocytes and endothelial cell alike.\n8. There is no fenestrated control of monocyte extravasation into the hepatic spheroid either, as happens in the liver (extravasation through tight HUVEC microvessel is different)."}, "author_response": "We thank the reviewer for the positive feedback and constructive review, which has greatly strengthened the revised manuscript. Please find our detailed responses below, with corresponding changes within the manuscript highlighted in yellow.\n1. Authors should comment on their choice to use human primary liver endothelial cells and stellates instead of HUVEC and lung fibroblasts and\nWe appreciate the reviewer’s attention to using organ-specific cell types for our system. We developed our MPS model with HUVECs and lung fibroblasts because they are canonically used in in vitro models that involve self-assembled 3D vascular networks, as we noted in the first paragraph of the results section (see also ref 42: Kim et al. 2013 - https://doi.org/10.1039/C3LC41320A). We believed this choice of endothelial and stromal cells would help us establish foundational protocols for future, improved models of hepatic insulin resistance, given that this study is — to our knowledge — the first to demonstrate the application of a vascularized liver MPS model to study insulin resistance.\nIn an effort to improve the physiological relevance of our system, we did explore replacing the lung fibroblasts within our spheroids with primary human stellate cells (HSCs) obtained from the same primary cell donor. While the HSC spheroids were able to aggregate and be incorporated with perfusable vessels (Fig R1A, B), we saw lower albumin production and diminished CYP3A4 activity in these devices, compared to ones with lung fibroblast-containing spheroids (Fig R1C, D). In addition, preliminary experiments revealed that several inflammation-associated cytokines were upregulated in HSC-containing devices, such as VCAM-1, TIMP-1, RANTES, and IL-6 (Fig R1E). These results were not surprising, given that standard methods for expanding primary HSCs on plastic are known to induce their activation towards a disease-associated myofibroblast phenotype (Friedman 2008 - https://doi.org/10.1152/physrev.00013.2007). Overall, the liver MPS model we developed is amenable to HSC co-culture, but substantial protocol development is required in the field to ensure that HSCs used in disease models start in a quiescent state — thus, we chose not to use HSCs for this current manuscript while we continue to characterize their effect in this system. We also note that our group is actively working on transitioning our liver models to use a tunable synthetic hydrogel that allows for modular control of biophysical and biochemical properties, which could help us avoid baseline stellate cell activation by more closely mimicking the native liver microenvironment. This work will be featured in a future manuscript.\nWe have modified the manuscript text to include more discussion of cell choice as follows:\nDiscussion, third paragraph: Incorporating these specialized cell types is the subject of ongoing work and will require further protocol optimization. For instance, primary human LSECs are notoriously challenging to properly isolate and validate,64 while standard methods for in vitro culture of stellate cells are known to result in their activation.65 Additionally, longer-term studies of vascular function would require continuous, unidirectional flow and replacement of fibrin with a synthetic extracellular matrix that more robustly withstands cell-mediated remodeling and flow conditions — modifications we are currently implementing.\nFigure R1: Human stellate cell (HSC)-containing spheroids display reduced hepatic function and altered inflammatory state. A: Hepatic spheroids form with HSCs in a 10:5:1:1 ratio of hepatocytes, human umbilical vein endothelial cells (HUVECs), Kupffer cells (KCs), and HSCs, with HSCs staining positive for alpha-smooth muscle actin (a-SMA). B: Brightfield image on Day 13 of device culture shows HSC spheroids integrate with vasculature, and vasculature likely displays open lumens evidenced by the clearance of cellular debris. Scale bar = 500 µm. C: Albumin secretion is reduced in HSC spheroid devices (“+HSC” conditions), compared to devices with standard spheroids made with NHLFs. D: CYP3A4 activity assayed on Day 11 in culture shows reduced activity in HSC spheroid devices. E: Heatmap of significantly differential features assayed on cell supernatant from HSC and NHLF spheroid devices, identified by BH-corrected Wilcoxon test p-value <0.05, colored by row-normalized z-score, with n=6 devices per condition.\n2. Authors mention that ‘Microvascular networks both surround and penetrate through the hepatic spheroids, establishing the spheroids as both interacting with and integrated within the microvasculature (Fig 1B).’ Surrounding is clearly visible in the provided pictures, is not clear however if they are penetrated or penetrating. Authors should provide more convincing evidence of this or rephrase.\nWe appreciate this note and agree that the maximum intensity projection image shown in Fig 1B is not sufficient evidence by itself for demonstrating penetration of the spheroids by the vessels. For improved clarity, we have removed the word “penetrate” from the text and SI. The modified sections read as follows:\nResults, “Multicellular hepatic spheroids integrate with self-organized vascular networks,” third paragraph: The microvascular networks surround the hepatic spheroids, establishing the spheroids as both interacting with and integrated within the microvasculature (Fig 1B).\nFigure 1B caption: Self-organized vascular network formation after 6 days in devices. Maximum intensity projection of live image taken at 10x magnification shows GFP-HUVEC networks (green) interacting with hepatocytes labeled with CellTracker Deep Red (magenta). Inset (yellow box) shows a magnified view, with arrows indicating regions where networks are in close proximity with spheroids. Scale bars = 200 µm.\n3. In relation to results from the glucose output assay: Is glucose output somehow normalized, ie per cell number, per protein or DNA content? The authors refer to these results as glucose production, yet the starting concentration of glucose in the gluconeogenic media is 50µM glucose – wouldn’t the graph in the figure 3C represent rather glucose consumption over 24h as the final concentrations are below initial input?\nIs there any glucose retained in the ECM or microfluidic channels during high gluceose exposre (IR condition) that could the low glucose exposure findings? Authors should include a cell free condition to rule this out.\nA 24h timeline for gluconeogenesis is rather long, especially for co-culture with other cell types, that rely on glycolysis.\nAuthors acknowledge in the result section that glucose production is lower than based on hepatocyte-only 3D culture. which is not in the dataset, nor has references. This part requires data or better data annotation (is it in SFig5 E?) or should be moved to the discussion section. Also, please clarify if this hepatocyte-only 3D culture is performed in the same microfluidic device.\nWe greatly appreciate the reviewer’s comments and suggestions to improve the utility of our glucose output assay and the clarity of our findings.\n(1) Regarding the comment that 24 hours may be too long of a timeline for measuring hepatocyte glucose production in this co-culture system:\nThis suggestion to consider a shorter timeline was extremely helpful! We decided to repeat the glucose production assay with three shorter timepoints of 2 hours, 4 hours, and 16 hours. We indeed saw that net glucose measurements had a time dependence, with 2 hours of incubation resulting in the reading that most clearly exhibits glucose production in the model beyond consumption from the cells in co-culture (Fig R2A-C).\nWe found that this shorter timeline did give us a more accurate reading of glucose production that was less influenced by consumption by other cells. The graph below displays net glucose concentration (i.e., the starting value of 50µM has been subtracted from all measurements), and we can confirm that there is indeed increased glucose production (i.e., positive values) in the IR condition. We also included devices that were stimulated with 5nM insulin, as suggested in the reviewer’s comment #5 (see below), and confirmed that the IR condition also leads to impaired suppression of hepatocyte glucose production by insulin (Fig R2D).\nFigure R2: Reduced glucose assay timepoints allow for improved measurement of hepatic glucose production. A: Total glucose measured after 2 hours, B: 4 hours, and C: 16 hours shows glucose levels decrease over time, though elevated levels in IR conditions are maintained. D: Glucose output measured after 2 hours in culture and corrected for glucose output media initial concentration shows elevated glucose in IR conditions, both with and without insulin stimulation, compared to the physiological conditions.\n(2) Regarding the question of whether our lower glucose readings could be the result of retention in the ECM or microfluidic channels:\nWe followed the reviewer’s suggestion to include a cell-free control to confirm there is no glucose retained in the fibrin hydrogel or device channels. We seeded devices with cell-free fibrin, changed media daily for 12 days, rinsed these devices with glucose-free media 3 times, and then switched to the “glucose production media” (containing 50µM glucose) for 2 hours (see Methods). We confirmed that glucose levels remained at the expected 50µM for both the physiological and IR medium formulations, indicating that there was no significant glucose retention in the hydrogel or PDMS, even for the IR condition that started out with higher glucose before the switch to glucose production media (SI Fig 8A).\n(3) Regarding the request for clarification about the phrase “hepatocyte-only 3D culture”:\nWe agree that we should be more clear here. We are referring to our observation that glucose production was higher in multicellular hepatic spheroids that were cultured in alginate wells as a comparison to our microfluidic devices (SI Fig 5).\nTo address the above points, we added the cell-free control and 2-hour glucose output data to a new supplemental figure (SI Fig 8) and have modified the manuscript text as follows:\nResults, “Vascularized liver platform recapitulates known features of insulin resistance disease state,” second paragraph: To establish whether this altered gene expression was associated with observable functional changes, we adapted a canonical assay to measure hepatocellular glucose output in a vascularized system, in which media is changed to a reduced glucose production media containing pyruvate and lactate (see Methods) for 24 hours49–51. The IR condition resulted in higher net glucose production compared to the physiological condition, indicating impaired metabolic regulation (Fig 3C). We note that with this 24-hour time scale, the absolute magnitude of glucose output observed in device culture is lower than expected, compared to alginate microwell culture of multicellular hepatic spheroids (SI Fig 5E). However, at 2 hours after culture in glucose production media, we found that the amount of glucose measured was more consistent with spheroid-only conditions (SI Fig 8B). These observations are likely due to the glycolytic metabolism resulting in glucose consumption by vascular cells in co-culture. Even with this added complexity to the system, the greater net glucose in IR conditions — even after insulin stimulation (SI Fig 8B, C) — is consistent with the observed metabolic gene expression changes.\nSI Figure 8: Glucose output measured at an earlier time point reveals elevated hepatocyte production and differential insulin response.\nA: Cell-free devices seeded with fibrin and cultured in physiological or IR media for 12 days before washing and incubating with 50µM glucose production media show no significant retention of glucose, n=2 devices per media.\nB: Glucose output assayed after 2 hours shows elevated glucose levels in IR media both with and without insulin stimulation. Physiological conditions stimulated with insulin had almost no detectable glucose after 2 hours in n=3 devices.\nC: Gene expression measured via RT-qPCR at the end of this 2 hour assay shows elevated metabolic gene expression in IR devices in glucose production media. Devices stimulated with 5nM Insulin show strongly decreased PCK1 and G6PC, though to a lesser degree in IR devices. FASN and FABP1 show a weaker response to insulin, with no differences between IR and physiological devices, n=3 devices for each condition.\n4. In Figure 4 Authors show that IR media condition cause reduced vessel coverage. Would this finding suggest less endothelial cells in IR conditions? If yes, is there a possibility that this higher glucose concentration in IR media condition is a result of less cells consuming glucose provided/or produced? This possibility should be addressed by the authors.\nWe appreciate the reviewer raising this consideration, and we would be avidly interested in pursuing this question further in future work. At present, the protocols we have developed are not sufficiently able to answer this question, due to difficulties in quantitative proliferation assays and high-throughput 3D imaging required to accurately assess differences in endothelial cell number. However, after we performed the reviewer’s suggested shorter time point glucose assay modification and observed the resultant elevated glucose production above the initial concentration, it remains likely that elevated hepatic production is significantly contributing to the high IR glucose concentration measured.\nWe have added the following sentence to the manuscript to raise this interesting possibility of an interplay between changes in both glucose produced and glucose consumed:\nDiscussion, sixth paragraph: Additionally, when these complex systems are maintained over multiple weeks, changes in the proportions of each cell type may also affect metabolic metrics such as the balance of glucose production versus consumption, underscoring the value of future studies that can examine these dynamics with higher spatiotemporal resolution.\n5. Hepatic insulin resistance is usually shown through impaired suppression of hepatocyte glucose production by insulin. Ideally this feature should be shown in the device in the similar setup as in SFig5E. Is this elevated glucose output in IR conditions not sensitive to 5nM Insulin exposure?\nWe thank the reviewer for noting that we can strengthen our findings by showing that our IR media formulation results in impaired glucose production even after insulin stimulation. We performed the assay as recommended (using the 2-hour time point, as suggested in the reviewer’s comment #3 above) and confirmed this.\nAs mentioned above in our response to comment #3, we have also modified the text to include this result as follows:\nResults, “Vascularized liver platform recapitulates known features of insulin resistance disease state,” second paragraph: We note that with this 24-hour time scale, the absolute magnitude of glucose output observed in device culture is lower than expected, compared to alginate microwell culture of multicellular hepatic spheroids (SI Fig 5E). However, at 2 hours after culture in glucose production media, we found that the amount of glucose measured was more consistent with spheroid-only conditions (SI Fig 8B). These observations are likely due to the glycolytic metabolism resulting in glucose consumption by vascular cells in co-culture. Even with this added complexity to the system, the greater net glucose in IR conditions — even after insulin stimulation (SI Fig 8B, C) — is consistent with the observed metabolic gene expression changes.\n6. Related to qPCR data: On which day is the expression profiling performed? How many independent experiments are represented in the graph Fig3B? What is the contribution of different cell types to the overall gene expression?\nWe thank the reviewer for helping us improve the clarity of these data. The graphs in Fig 3B were generated from two independent experiments on 2 devices from each experiment, where we performed qPCR on devices that had been cultured in each medium formulation for 12 days. We have updated the figure caption to reflect this.\nThe authors additionally show the expression profile of the vascular network without spheroids. However, this comparison alone does not allow for a direct deduction of the relative contribution of spheroids to gene expression levels. When different cell types interact, their gene expression profiles are likely to change dynamically in response to signaling cues, flow, ecm- and cell-cell communication. Authors should address it or use more appropriate approach.\nWe agree that cell-cell interactions, biomechanical forces, and other signaling cues can directly influence gene expression and thus are important factors to consider. In the case of our vascular networks-only conditions, we maintained the same media, hydrogel, and flow profiles as the full liver MPS to best control for some of these facets. However, it is also important to consider that specialized functions also result in cell-type specific gene expression beyond these environmental cues. Given literature regarding hepatic gene expression and analysis of existing single-cell transcriptomics datasets, there is reason to expect the metabolic genes measured in this study are predominantly expressed by hepatocytes in this system, due to their unique role in metabolic regulation (Fig R3), though it is true that hepatic gene expression is still affected by the factors listed above.\nWe have also more directly highlighted this point in our manuscript as follows:\nDiscussion, seventh paragraph: As we move towards deeper characterization of complex disease models — and ultimately, their adoption for new mechanistic studies — it will also be important to carefully consider which assays and experimental design schemes allow us to accurately assess the contribution of specific cell populations. For instance, gene expression profiles are influenced not just by the presence of constituent cell types, but also cell-cell interactions and environmental cues such as flow and ECM-mediated signaling.\nFigure R3: Metabolic genes of interest are predominantly expressed by hepatocytes in a human liver transcriptomics dataset. Single-cell RNA seq data of uninjured (gray) and cirrhotic (pink) human liver samples (Ramachandran et al. 2019 - https://doi.org/10.1038/s41586-019-1631-3) shows hepatocytes are the cell type primarily expressing the metabolic genes of interest for this manuscript.\nFurthermore, how would the gene expression profile change under the low glucose condition presented in Fig3C?\nWe have measured the gene expression profile of our vascularized liver MPS under these low glucose conditions after 2 hours, as shown above in SI Fig 8C. We see similar results as before, with elevated expression of metabolic genes in the IR condition. Though all samples began in the same glucose condition, the corresponding glucose output measurements indicate hepatic production of glucose in IR conditions, aligning with the observed gene expression profiles.\n7. Related to Insulin ELISA:\nAuthors show the insulin clearance fraction in relation to the cell-free controls. Is there any insulin binding to the ECM/microfluidics? If any, what is the dynamic of this binding?\nWe appreciate the reviewer’s suggestion of including a cell-free control condition to confirm there is no insulin binding to the fibrin hydrogel and microfluidic devices, as this is important to verify. We ran an additional experiment where we seeded devices with cell-free fibrin and changed media daily in the same manner as our liver MPS devices. We saw no change over time in the insulin levels measured from these cell-free samples in either media condition. This cell-free control data is now incorporated into the manuscript as SI Fig 7C (also shown below).\nWhat is the volume collected from the device for the insulin ELISA? Is the undiluted sample measured or diluted further before ELISA?\nWe collect approximately 150uL of media from each device, which is frozen and divided between assays of interest. In the case of the insulin ELISA, physiological samples are diluted 1:2, and IR samples are diluted between 1:3 - 1:5 to keep them within a similar range on the assay’s standard curve. We followed the manufacturer recommended standard curve range from 1000 pmol/L- 15.6 pmol/L.\nWe have edited the text of our methods section as follows to detail these dilutions for the reader:\nInsulin Clearance Quantification\nInsulin clearance was evaluated from cell supernatant via ELISA (R&D, DY8056). Physiological media samples were diluted 1:2, while IR media samples were diluted between 1:3-1:5 for each insulin ELISA. Clearance fraction was calculated by dividing the measured insulin concentration in cultured media by the measured insulin concentration in cell-free control wells.\nInsulin clearance in the “physiological” conditions show very small SD between the 8 replicates. If on average 80% of the initial concentration is cleared, the measured values will be around 40pM. If sample is diluted this can get the values very close to the lower limit of the assay. Is this result (low variation) a biological phenomenon or rather related to the values of ELISA getting close to the lower limit of detection? Assaying the IR condition seems to yield bigger SD. Is that related to the insulin values being measured in the ELISA assay range for this kit?\nWe appreciate the reviewer's careful attention to this dataset and observation of the changes in variance between the low and high insulin media conditions. The limit of detection for the kit used in this study is 15.6pM based on the lowest value on the standard curve, and we ensured that all samples reported here are within the ELISA assay range of this kit, though it’s true that physiological samples fall on the lower end of this assay. One possible contribution for this smaller SD observed is changes in the kinetics of insulin clearance in a concentration-dependent manner. Previous work from our group has shown that hepatocyte insulin clearance follows a first-order rate, with the insulin clearance slowing as the insulin concentration falls in the system (Hellen & Ungerleider et al. 2025 - https://doi.org/10.1101/2025.01.08.631261). One hypothesis is that this may lead to insulin levels approaching a plateau of insulin cleared as the rate changes at the low insulin concentrations measured after 24 hours in physiological media conditions, ultimately contributing to a smaller SD between samples.\n8. Related to Luminex data. Fig 3 D shows Luminex panel for samples taken at the 10 day of culture. Although a heatmap with row normalization neatly shows the results it would be good to understand for the reader what is the actual spread of the data. This is an interesting data set and it is important to provide this detail. Providing supplementary data (e.g. in a form of table) with concentration per analyte per sample would give that full picture on what to expect from this model in terms of concentration range per analyte.\nThank you, we appreciate this suggestion and agree that showing the concentration per sample for each analyte would make this dataset more interesting and useful for our readers.\nWe have added the following graphs as a new supplemental figure (SI Fig 9), showing univariate plots for each analyte to illustrate the spread of the data points. The text has also been modified to reference this new figure:\nResults, “Vascularized liver platform recapitulates known features of insulin resistance disease state,” third paragraph: This panel was assayed via Luminex on supernatant collected on day 10 of device culture (Fig 3D, SI Fig 9).\nSI Figure 9: Univariate concentrations of analytes measured on supernatant devices after 10 days in culture. Concentrations measured via Luminex of devices cultured in physiological or IR conditions with n = 22 devices per condition across N = 3 experiments.\n9. Reproducibility: all results only list the technical replicate number (n), but not the biological/independent experiment number (N). Wouldn't it be beneficial to conduct at least three independent experiments for model validation? How reproducible can the data be considered?\nWe thank the reviewer for their attention to this important distinction and appreciate the importance of reproducibility of this study. The data presented in the main text do represent multiple independent experiments, and we have updated the figure captions for each figure to reflect both the number of devices (n) and the number of independent experiments (N). This information can also be found described in the reporting summary associated with this manuscript.\nBeyond this, Reviewer 2 made the helpful suggestion of validating this model with multiple primary human liver cell donors to further ensure reproducibility of our findings, given the known variation between primary hepatocytes. As a result, we have added the new SI Fig 6, which validates that the liver MPS model is reproducible in an independent experiment using two new cell donors’ hepatocytes and Kupffer cells. We have also added the new SI Fig 7 validating changes in insulin clearance and glucose output with these new donor cells cultured in physiological vs. IR media conditions. Please find the full response detailing these results in Reviewer 2’s comment #2 below.\n10. Can the authors say something about stability over time of the Kupffer cells in 14 days of device culture? They are present at day 14 but did they increase/decrease over time?\nAt present, we don’t have quantitative data on how the number of Kupffer cells is changing over time. However, we appreciate this question, as further analysis of Kupffer cells in our MPS models is an active area of protocol development in our lab right now. We attempted one experiment where we labeled the Kupffer cells with CellTracker dye and took images on day 4 of device culture (Fig R4). However, we realized that because there are relatively few Kupffer cells in our system, properly quantifying them across devices and conditions would require multiple high-resolution, tiled, live-imaging sessions, which is prohibitively complex with our current equipment/setup. We look forward to exploring this question of Kupffer cell stability in future work.\nFigure R4: Kupffer cells in spheroids can be monitored via CellTracker labeling. Two representative maximum intensity projection images of hepatic spheroids integrated with vessel structures on day 4 of device culture. Spheroids are visible in brightfield; human umbilical vein endothelial cells (HUVECs) are labeled with GFP; and Kupffer cells are labeled with CellTracker Deep Red. Scale bars = 100µm.\n11. Suppl. fig 3A seems to show a lower density spheroids compared to suppl figure 4. What is the distribution and variation in density of spheroids in MPS system?\nWe agree it is very important to consider inter- and intra-experiment variability, especially when using primary cells in increasingly complex models of multifaceted diseases. Yes, there is inherent variability in the number and density of spheroids based on our seeding protocols, as shown in the quantification below (Fig R5) for an experiment that contained n = 11 or 12 devices for each of the two new donors we characterized in our revision experiments. To ensure that this variability does not unintentionally skew our results, we inspect all devices after seeding and distribute them across our media conditions. With this step, we see that even with a range of possible numbers, the average number of spheroids per device is consistent across media conditions.\nWe also incorporated this data on spheroids per device for the two new donors into the new SI Fig 6D and 6K.\nFigure R5: Number of spheroids counted per microfluidic device, where each dot represents one device. n = 11 or 12 devices per media condition for each donor.\nMinor comments:\n1. A method description for the spheroid-only dataset is missing.\nWe appreciate this note and have added the following subsection to the Methods section:\nMulticellular Hepatic Spheroid Culture\nFor some experiments, multicellular hepatic spheroids were maintained in alginate wells as a spheroid-only control for our vascularized MPS model. After 48 hours of aggregation, spheroids were switched into either the physiological or IR medium formulation and maintained for 12 additional days. Media changes (300µL per well) were performed every 48 hours according to previously published protocols.40\n2. Spheroids are made from 120,000 primary human hepatocytes, 60,000 HUVECs, 12,000 Kupffer cells, and 12,000 NHLFs were pre-mixed to a volume of 75µL per well. Authors should clarify number of spheroids made and number of cells that are in these spheroids.\nThank you, we have updated our methods section as follows:\nMulticellular Hepatic Spheroid Formation\nSpheroids were formed using alginate microwells as previously published.40 Alginate microwells were primed with 5% BSA for at least 2 hours at 37°C before seeding. 120,000 primary human hepatocytes, 60,000 HUVECs, 12,000 Kupffer cells, and 12,000 NHLFs were pre-mixed to a volume of 75µL per well. After removing the BSA solution, alginate microwells were primed with 75µL of culture media, then the spheroid cell mixture was added dropwise directly onto the alginate microwells. In total, each alginate well contains 600 spheroids, where each spheroid is composed of approximately 200 hepatocytes, 100 HUVECs, 20 Kupffer cells, and 20 NHLFs (with some inherent level of variability).\n3. Please, add to methods section at which day of aggregation spheroids were added to the device. Is it always day 2 (48h post aggregation)? Please, add for better clarity.\nThank you, we have modified the methods section as follows:\nMethods, Device Seeding: After 48 hours of aggregation in alginate wells, spheroids were harvested from alginate wells by pipetting with a wide bore pipette tip and incubated with AlgiMatrix Dissolving Buffer (Gibco, A11340) for 5 minutes before spinning down at 50g for 2 minutes.\n4. Add individual data values in the graphs in Fig 3A, Fig2C\nFor the albumin and insulin clearance time series datasets, we chose to present the data as average measurements across devices for ease of visualization, due to the large number of datapoints (e.g., the main text figures include n = 11 devices for albumin and n = 8 devices for insulin, for each media condition). However, we do appreciate that it is informative to see individual traces to understand variability, so we have included two examples below that show a side-by-side comparison of our albumin (SI Fig 13) and insulin clearance (Fig R6) data for individual devices vs. averaged measurements for all three primary liver cell donors that were used in this study.\nWe have included the individual traces of albumin secretion for each donor as SI Fig 13 and reference this new figure in our discussion about device variability as follows:\nDiscussion: While incorporating a greater number of cell types may ultimately improve physiological relevance of the system, it also complicates interpretations of experimental results, requiring additional specialized assays to parse cell type-specific contributions, especially given the inherent variability across devices (SI Fig 13).\nSI Figure 13: Liver MPS exhibits device-to-device variability. Albumin secretion over time, shown either as an average across devices or as individual traces per device for three primary liver cell donors: Donor #1 (A vs. B), Donor #2 (C vs. D), and Donor #3 (E vs. F).\nFigure R6: Insulin clearance data show liver MPS exhibits device-to-device variability. Insulin clearance over time, shown either as an average across devices or as individual traces per device for three primary liver cell donors: Donor #1 (A vs. B), Donor #2 (C vs. D), and Donor #3 (E vs. F).\n5. Glucose Output Assay section; should be “50µM glucose” instead of “50µm glucose”\nWe have corrected this typo in the text and appreciate the catch!\n6. Hepatocyte secretion of albumin, normalized per million hepatocytes, shows increased albumin levels over time, n = 11 devices per media condition. How was this correlated? With seeded hepatocytes? Was there variation in size and number hepatocytes in the spheroids? Spheroids-only secreted at day 3 around 5ug/day/106 cells seeded. In the chip it is after 2 days of culture already 10 times higher. Is the data of same hepatocyte donor? Do the authors have an explanation?\nWe appreciate the reviewer’s questions to improve the clarity of our methods and findings. Yes, this albumin normalization was correlated using the observed seeded number of spheroids for each device. We approximate this by taking a whole-device image after seeding followed by image quantification to count the number of spheroids and further approximate the number of hepatocytes using the average number of hepatocytes per spheroid (see Methods, “Multicellular Hepatic Spheroid Formation”). This normalization per million hepatocytes was performed to allow for comparison to other hepatocyte studies that follow this convention. However, there is a mild degree of natural variation in spheroid size and number per device.\nIn regards to the comparison with the spheroid-only control: yes, these data were from the same hepatocyte donor. A combination of factors may contribute to the elevated albumin levels, with some possibilities listed below:\n● Flow: Several groups have reported that shear stress enhances hepatocyte function (e.g., Li et al. 2020 - https://doi.org/10.1152/ajpgi.00379.2019 and Chhabra et al. 2022 - https://doi.org/10.1073/pnas.2115867119). In our experiments, the spheroid plates are kept static throughout culture, while the devices are on a rocker platform that creates minor interstitial flow prior to vascular network formation and luminal flow after the vessel structures become lumenized.\n● Hepatocyte-endothelial cell interactions: Related to the above point, it is well-established that co-culture with endothelial cells, especially in a dynamic environment with flow, can enhance hepatocyte albumin secretion (e.g., Lee et al. 2004 - https://doi.org/10.1016/j.bej.2003.07.005, Guzzardi et al. 2009 - https://doi.org/10.1089/ten.tea.2008.0695, Kim et al. 2010 - https://doi.org/10.1371/journal.pone.0015456). Thus, it would make sense that the presence of perfused endothelial networks in the device culture format would lead to increased hepatocyte albumin production.\n● Media change frequency and volume: Following standard media change schemes for microfluidic devices and alginate microwell hepatocyte culture, each device (containing up to ~200 spheroids) receives a daily media change of 150µL, while each alginate well (containing ~600 spheroids) receives a media change of 300µL every other day. The hepatocytes in devices may thus be exposed to more nutrients that promote enhanced function, though it is difficult to draw definitive conclusions with the presence of the vascular cells in devices as well.\n● 3D culture environment: The spheroids in alginate wells are not cultured with any added extracellular matrix, while the spheroids in devices are embedded within a fibrin hydrogel. One study has shown that adding fibrinogen to in vitro cultures of hepatic spheroids improves liver function by promoting cellular aggregation and enhancing signal transduction (Li et al. 2021 - https://doi.org/10.1016/j.biomaterials.2021.121266).\n● Cell count normalization: As noted above, several normalizations are performed for hepatocytes in devices in order to obtain a value that can be compared with conventional reports of albumin secretion. Due to the natural experimental variability in number of spheroids per device and number of hepatocytes per spheroid, it is possible that propagation of error across these normalizations may be a factor contributing to the higher albumin values in devices."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have done an outstanding job addressing my comments with textual changes, additional hepatocyte donor data, and overall just better justifying their design choices. The platform has the promise to be built on further, but the findings in this manuscript are novel and deserve publication in its current revised state.", "reviewer_3": "Reviewer 1 commented on the choice of lung fibroblasts and HUVEC cells. The authors respond that these simple cell models would help to establish foundational protocols. However, the attempt to replace lung fibroblasts with primary cells resulted in reduced functionality and it is acknowledged that substantial protocol adjustments will be required to culture more physiologically relevant cell models. This is a main limitation of the work and I question the relevance and translational potential of mimicking intricate physiological processes, such as insulin resistance, using “unphysiological” cell models.\nThis concern is further exemplified by the third comment. The used cell lines rely on glycolysis, which could majorly impact the results of gluconeogenesis assays. While the shorter timepoints add information, the conceptual concern remains.\nAlso, even among primary endothelial cells there are huge differences with regards to phenotypes and molecular signatures. Further characterization of the cell models is required to understand how similar HUVEC cells in the presented setup are compared to liver sinusoidal endothelial cells with regards to signaling and barrier functon(fenestrated vs non-fenestrated vessels).\nData on Kupffer cell proliferation, stability and activation remain lacking.\nI also agree with concerns raised by Reviewer 2 regarding the lack of novelty. The authors argue that the incorporation of vasculature would result in a more physiological system; however, this is only true if the vasculature is indeed integrated in a way that allows to mimic its physiological function both with regards to signaling and barrier. A co-culture with HUVEC cells does not fulfill this requirement.\nI also share the concern that the added value should be demonstrated compared to transwell cultures. It is argued that transwell cultures have several limitations, which is true; however, this is also true for the approach presented here. As such, it remains important to show that the new strategy adds value compared to the conventional transwell systems."}, "author_response": "Rebuttal Letter for NCOMMS-25-05495A: “A Vascularized Liver Microphysiological System Captures Key Features of Hepatic Insulin Resistance and Monocyte Infiltration”\nSeptember 24, 2025\nPlease find below our detailed, point-by-point response to the comments provided by our referees, with their comments copied in purple text and our responses written in black text. To provide additional emphasis on certain points, we have also made changes to the manuscript text since the first revision, which are highlighted in the revised manuscript document as well as noted in our response below. We hope that this response and the newly revised manuscript clarify the issues raised, emphasize the novelty and advantages of our approach, and underscore our commitment to further improving upon our model in future work.\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have done an outstanding job addressing my comments with textual changes, additional hepatocyte donor data, and overall just better justifying their design choices. The platform has the promise to be built on further, but the findings in this manuscript are novel and deserve publication in its current revised state.\nWe thank the reviewer for their positive assessment of our revised manuscript and their recommendation for publication. We greatly appreciate their thoughtful suggestions and the requests for clarification in their original review, which helped us significantly improve our manuscript.\nReviewer #3 (Remarks to the Author):\nWe are very grateful to the reviewer for stepping in on short notice in this second round of review, to provide timely feedback on our response to the first round of review following the withdrawal of Reviewer 1. However, based on their comments, we believe the significance of our findings may not have been fully appreciated. In brief, our work represents a significant advance in modeling human liver pathophysiology, as it is the first — to our knowledge — that captures local hepatocyte-immune-microvascular interactions in an accessible microfluidic platform. More specifically, our primary human cell-based MPS model not only recapitulates both metabolic and immune hallmarks of hepatic insulin resistance, but also uniquely features the physical integration of hepatic tissue aggregates with perfusable, self-assembled microvasculature exhibiting barrier integrity and signaling function. This functional vasculature directly enables the study of clinically relevant immune-tissue interactions, such as enhanced monocyte recruitment under insulin resistance conditions, offering an important new tool for advancing investigation of complex metabolic disease.\nWe have prepared a detailed response to the reviewer’s comments below, which we hope will help clarify the issues raised, support a more complete evaluation of the revised work, and further reinforce the value of our contributions.\n1. Reviewer 1 commented on the choice of lung fibroblasts and HUVEC cells. The authors respond that these simple cell models would help to establish foundational protocols. However, the attempt to replace lung fibroblasts with primary cells resulted in reduced functionality and it is acknowledged that substantial protocol adjustments will be required to culture more physiologically relevant cell models. This is a main limitation of the work and I question the relevance and translational potential of mimicking intricate physiological processes, such as insulin resistance, using “unphysiological” cell models.\nWe fully acknowledge the limitations of using non-liver-specific cells and addressed this point in both our original response letter and the revised manuscript. In response to Reviewer 1’s request, we expanded the discussion in our first revision to underscore that incorporating primary liver cells into complex in vitro models remains a major challenge across the field:\n“Incorporating these specialized cell types is the subject of ongoing work and will require further protocol optimization. For instance, primary human LSECs are notoriously challenging to properly isolate and validate,64 while standard methods for in vitro culture of stellate cells are known to result in their activation.65”\nAgainst this backdrop, our central advance is demonstrating that primary human cell-based liver aggregates can be vascularized in vitro, with fully perfusable vessel structures that allow immune cell trafficking.\nAs part of our response letter, we included data (Fig R1) confirming that primary human stellate cells (HSCs), when expanded on plastic, led to reduced hepatic function when incorporated into our spheroids. While the reviewer points this out as a main limitation of our work, we note that this effect was only observed in the context of direct comparison with our alternative fibroblast population — underscoring that models using less relevant cell lines can be important tools when primary cells are unable to maintain their physiological baseline in vitro. Importantly, we demonstrate that HSC-containing spheroids can still integrate with perfusable vasculature (Fig. R1B), which showcases that our system is poised to accommodate more physiologically relevant HSC co-culture once baseline activation issues are resolved — a challenge that we and others are actively working to overcome.\nWe also agree with the reviewer that insulin resistance is a complex, multifaceted disease state. For this reason, we employed several established metrics to confirm that our vascularized liver MPS can capture multiple known phenotypes associated with insulin resistance. These include reduced insulin clearance (Fig 3A), increased expression of gluconeogenesis-associated genes (Fig 3B), elevated glucose production (Fig 3C), increased secretion of pro-inflammatory cytokines, chemokines, and growth factors (Fig 3D-F), and dysregulated vascular features (Fig 4A, C-E). We also note that Reviewer 2 initially raised similar concerns about the applicability of our model, and then subsequently judged that our revision appropriately addressed their concerns. Specifically, we confirmed that disease phenotypes were recapitulated across three different hepatocyte and Kupffer cell donors, highlighting that our findings are reproducible in primary cells known to experience high donor-to-donor variability (SI Fig 7, Fig R5, Fig R6).\n2. This concern is further exemplified by the third comment. The used cell lines rely on glycolysis, which could majorly impact the results of gluconeogenesis assays. While the shorter timepoints add information, the conceptual concern remains.\nThis appears to refer to Reviewer 1’s comment that glucose output in our co-culture system is influenced by the presence of cells that consume glucose. This is indeed an important consideration, given that the 24-hour gluconeogenesis assay we adapted for our studies was originally developed for hepatocyte monoculture. Because of the added complexity in our system, we explicitly refer to our measurements in the manuscript as net glucose levels rather than a direct readout of glucose production.\nIn their prior review, Reviewer 1 very helpfully suggested that 24 hours may be too long of an incubation period to properly parse the balance of glucose production vs. consumption in our co-culture context. As described in our original response letter, we performed the recommended experiment with shorter timepoints and found that after 2 hours (vs. 24), glucose levels rose above the starting concentration of 50μM (Fig R2A), confirming that we do see glucose production in our co-culture system. This clarification was incorporated into the revised manuscript (SI Fig 8). Moreover, we provide additional evidence that we can recapitulate facets of impaired gluconeogenesis in our insulin resistance condition through gene expression measurements (Fig. 3B, SI Fig 8C), as well as via demonstrating impaired insulin-mediated suppression of glucose production, as suggested by Reviewer 1 (SI Fig 8B-C).\nTo address Reviewer 3’s concern about our choice of “unphysiological” cells in the context of this assay: we note that liver-specific endothelial cells also perform glycolysis and would similarly influence net glucose measurements. Indeed, we view our vascularized system’s ability to capture the balance of both glucose production and consumption as a strength rather than a limitation — our model reflects physiologically relevant cell-cell interactions that simpler monoculture systems cannot replicate.\nWe have now modified the manuscript to clarify our findings and emphasize this advantage of our model, as follows:\nResults:\n“We note that with this 24-hour time scale, the absolute magnitude of glucose output observed in device culture is lower than in microwell culture of free hepatic spheroids (SI Fig 5E) — highlighting our MPS model’s ability to capture the physiologically relevant balance of glucose production by hepatocytes and consumption by vascular cells. To more specifically measure differences in glucose production, we performed a shorter version of the assay and confirmed that net glucose levels in our vascularized system align closely with those in our spheroid-only benchmark condition after 2 hours of culture in glucose production media (SI Fig 8). Notably, the greater net glucose levels in the IR condition — even after insulin stimulation — are consistent with the observed metabolic gene expression changes (SI Fig 8).”\nDiscussion:\n“Additionally, when these complex systems are maintained over multiple weeks, changes in the proportions of each cell type may affect overall metabolic metrics. As one example, we show that our vascularized liver MPS can capture the balance of glucose production versus consumption — this not only reflects our ability to recapitulate physiologically relevant cell-cell interactions that simpler monoculture systems cannot replicate, but also underscores the value of future studies that examine these dynamics with higher spatiotemporal resolution.”\n3. Also, even among primary endothelial cells there are huge differences with regards to phenotypes and molecular signatures. Further characterization of the cell models is required to understand how similar HUVEC cells in the presented setup are compared to liver sinusoidal endothelial cells with regards to signaling and barrier function(fenestrated vs non-fenestrated vessels).\nWe agree with the reviewer that there are important distinctions between liver sinusoidal endothelial cells and HUVECs. What motivated our choice of HUVECs — in addition to our earlier explanation that primary human LSECs are notoriously challenging to properly isolate and validate across the field — is that we are unaware of any microvascularized liver models that successfully incorporate human LSECs in a functional way. We realize that we did not fully describe this prior landscape in our first two submissions, leaving the reviewers without important contextual information for our work. Thus, please find a more detailed overview of the LSEC literature as follows:\nWe note that there are a number of reports on the generation of liver sinusoidal endothelial-like cells that are derived from induced pluripotent stem cells (iPSCs) or human embryonic stem cells (hESCs) (e.g., Wilhelmsen et al. 2023 - https://doi.org/10.3389/fbioe.2023.1223737; Gage et al. 2020 - https://doi.org/10.1016/j.stem.2020.06.007), with some studies showing their co-culture with iPSC-derived hepatocyte-like cells in planar flow devices (e.g., Danoy et al. 2023 - https://doi.org/10.1111/hepr.13893) or appearance in iPSC- or hESC-derived organoids (e.g., Lucendo-Villarin et al. 2020 - https://doi.org/10.1088/1758-5090/abbdb2; Zhang et al. 2025 - https://doi.org/10.1002/advs.202411667; Saiki et al. 2025 - https://doi.org/10.1038/s41551-025-01416-6). While these tissue models are promising, they are not perfusable in vitro and have not been accepted into mainstream use, as they do not sufficiently replicate postnatal liver in a way that gives confidence about translation from preclinical studies. Other groups have pursued spheroid co-cultures of mature donor-derived LSECs with hepatocytes (e.g., van Reit et al. 2024 - https://doi.org/10.1097/hc9.0000000000000374), but these spheroids have not been validated to contain perfusable vessels and are not linked to an external microvasculature. A third approach makes use of the commercial Emulate Organ-Chip, which has been developed for various applications over the last 15+ years and enables co-culture of different cell types on opposite sides of a membrane. A number of studies have used this chip for co-culturing human LSECs with hepatocytes and other nonparenchymal cells (e.g., et), but the compartment-based nature of this culture system precludes it from being able to capture the perfusable microvasculature state described in our work.\nOn the whole, the LSEC community is relatively small, and clearance of biologics by human liver remains an outstanding challenge that many industry investigators are eager to address. We are currently working with an industry partner who has expert perspective on this challenging problem, as he leads a preclinical ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) team focused on NAMs (Novel Alternative Methods); we would be glad to provide his contact information if that is desirable.\nWe wish to reiterate that a primary advance of our work lies in our model’s integration of primary human hepatic aggregates with perfusable vasculature, which directly enables investigation of clinically relevant immune-tissue interactions. As we take careful steps to ensure that we can incorporate rigorously validated, functional LSECs into these complex in vitro systems, we look forward to the additional insights we will gain into the immune and vascular features that accompany and contribute to metabolic dysregulation in the liver.\nTo include more of this context in our manuscript, we have modified the discussion to more clearly position our study within the landscape of liver MPS models and underscore the importance of prioritizing inclusion of functional primary NPCs in future studies:\n“In addition, this MPS could be applied to study liver-specific vascular features that accompany and contribute to metabolic dysregulation63, using LSECs and stellate cells in the spheroids instead of HUVECs and NHLFs, and liver-derived large vessel endothelial cells for the external vasculature. Presently, incorporating these liver-specific cell types into complex in vitro models remains a major challenge across the field; primary human LSECs are notoriously challenging to properly isolate and validate,64 while standard methods for in vitro culture of stellate cells are known to result in their activation.65 While a number of groups have reported liver models that include donor LSECs or LSEC-like cells derived from stem cell populations64-72, all of these in vitro systems lack an integrated, perfusable vasculature, ultimately limiting their translational potential. Our study addresses this gap: having demonstrated that we can achieve physical integration of hepatic tissue aggregates with perfusable, self-assembled microvasculature exhibiting barrier integrity and signaling function, we can now transition toward using liver-specific NPCs. As we and others take careful steps to ensure that we can incorporate rigorously validated, functional NPCs into these complex in vitro systems, we look forward to gaining additional insights into the immune and vascular features that accompany and contribute to metabolic dysregulation in the liver.”\n4. Data on Kupffer cell proliferation, stability and activation remain lacking.\nThis remark appears to follow our response to a comment from Reviewer 1, who asked:\n“Can the authors say something about stability over time of the Kupffer cells in 14 days of device culture? They are present at day 14 but did they increase/decrease over Time?”\nReviewer 1 did not request any characterization of Kupffer cell activation, so we are not clear on why Reviewer 3 would expect that to be a part of the response. With regards to proliferation and stability, we did perform additional live-cell imaging of Kupffer cells to investigate this (Fig R4 in the original response letter), and were able to validate that Kupffer cell numbers at earlier timepoints are comparable to those observed at Day 14. However, given the limited number of images, we believe this observation should not be over-interpreted and is better explored in future studies. As discussed in our manuscript, the relationship between peripheral immune cell recruitment and shifts in Kupffer cell population during metabolic liver disease remains incompletely understood (Kazankov et al. 2018 - https://doi.org/10.1038/s41575-018-0082-x; Blériot & Ginhoux 2019 - https://doi.org/10.3389/fimmu.2019.02694; Barreby, Chen & Aouadi 2022 - https://doi.org/10.1038/s41574-022-00675-6). For this reason, we believe that extensive analysis of Kupffer cell dynamics in our system would be premature, given the absence of clearer benchmarks in the field at this point in time. With our demonstration that integrating hepatic spheroids with perfusable vasculature enables the study of clinically relevant immune-tissue interactions, we believe our model is well positioned for deeper investigation of immune cell dynamics following recruitment and are excited to further pursue this avenue in future work.\nAdditionally, in light of Reviewer 3’s prior comments on the importance of using liver-specific cells and their concerns about the novelty of our system, we wish to emphasize that our inclusion of Kupffer cells is itself a notable advance. These cells are omitted in other vascularized liver models (e.g., Chhabra et al. 2022 - https://doi.org/10.1073/pnas.2115867119; Bonanini et al. 2022 - https://doi.org/10.1007/s10456-022-09842-9; Bonanini et al. 2025 - https://doi.org/10.1088/1758-5090/ad818a), with the final study referenced even highlighting the absence of Kupffer cells as a major limitation of their work.\nWe have added to our manuscript discussion as follows, in order to more clearly highlight this advance, as well as further emphasize Kupffer cell dynamics as an important direction for follow-up investigation:\n“While it has been clinically established that there is increased monocyte recruitment in metabolic liver diseases such as MASLD and MASH, the timing and relationship between the recruitment of this immune population and a loss of tissue-resident Kupffer cells remains incompletely understood.39,66,67 Given that our model is — to our knowledge — the first vascularized liver MPS to include donor-matched Kupffer cells27,28,30,75, this system is well positioned for deeper investigation of immune cell dynamics following recruitment.”\n5. I also agree with concerns raised by Reviewer 2 regarding the lack of novelty. The authors argue that the incorporation of vasculature would result in a more physiological system; however, this is only true if the vasculature is indeed integrated in a way that allows to mimic its physiological function both with regards to signaling and barrier. A co-culture with HUVEC cells does not fulfill this requirement.\nFirst, we note that Reviewer 2 ultimately endorsed publication of these findings. After a second look at the detailed landscape and data we provided in response to the first review, Reviewer 2 reversed their opinion of lack of novelty, writing:\n“The authors have done an outstanding job addressing my comments with textual changes, additional hepatocyte donor data, and overall just better justifying their design choices. The platform has the promise to be built on further, but the findings in this manuscript are novel and deserve publication in its current revised state.”\nWe therefore respectfully challenge Reviewer 3’s characterization of our model as a “co-culture with HUVEC cells” as a significant oversimplification of our system. Contrary to this new reviewer’s assertion, we provide direct evidence that the vasculature in our system is functionally integrated with human hepatocyte aggregates. We note that this work is the first, to our knowledge, to demonstrate such integration into a human hepatocyte aggregate, in contrast to publications where aggregates are suspended in a hydrogel, in the vicinity of vessels formed by a casting process (e.g., Chhabra et al. 2022 - https://doi.org/10.1073/pnas.2115867119; for a more detailed analysis of the advances our system makes beyond this particular study, please see our original response letter). Specifically, we show physical integration of self-assembled vessel structures with hepatic spheroids using fluorescently labeled cells (Fig 1B-C, SI Fig 3B, SI Fig 4C); confirm barrier function via dextran perfusion (Fig 2A-B, SI Fig 4A-C, SI Video 2) and permeability measurements (Fig 4E); establish that vascular cells contribute to specific disease phenotypes by comparing insulin clearance, gene expression, and inflammation profiles associated with the full liver MPS model vs. hepatic spheroid-only or vascular network-only controls (Fig 3, SI Fig 5, SI Fig 10); and demonstrate that this liver MPS enables monocyte perfusion through vessel structures as well as their extravasation into extravascular spaces (Fig 5B-D, SI Video 3).\nAs copied below, we have amended our abstract to center this advance and underscore what it enables:\n“To address this, we developed a microphysiological system (MPS) in which multicellular human hepatic spheroids are physically integrated with self-organized microvascular networks. We show extensive interaction between primary human spheroids and perfusable vasculature while maintaining key hepatic functions. … All together, this vascularized liver MPS model captures local hepatocyte- immune-microvascular interactions in an accessible microfluidic platform, enabling the study of clinically relevant immune-tissue interactions in complex metabolic disease.”\n6. I also share the concern that the added value should be demonstrated compared to transwell cultures. It is argued that transwell cultures have several limitations, which is true; however, this is also true for the approach presented here. As such, it remains important to show that the new strategy adds value compared to the conventional transwell systems.\nThis appears to refer to Reviewer 2’s comment that a transwell co-culture system — endothelial cells grown on transwells that are placed atop hepatic spheroids — could serve as an alternative permeability assay that would have enabled higher throughput studies. In our response letter, we noted that our group has shown that permeability values from 2D endothelial monolayers and 3D microvascular networks differ by multiple orders of magnitude, where the 3D culture format yields values that are more comparable to in vivo measurements. We also cited studies showing that endothelial barrier function is influenced by substrate stiffness and direct co-culture with stromal cells. These findings collectively support that our liver MPS is fundamentally different from a conventional transwell system — where endothelial monolayers are not in contact with hepatic spheroids — making such a comparison inappropriate and less physiologically relevant. We also note that Reviewer 2 did not raise this concern again in their response to our revisions, writing that we had now provided satisfactory justification of our design choices."}, {"round": 3, "reviewer_comments": {"reviewer_3": "I thank the authors for the additional clarifications and I apologize if my previous comments gave the impression that I would not consider the presented results novel per se. Overall, I appreciate the considerable novelty in bioengineering and method development; however, I respectfully maintain the opinion that this work does not provide a “significant advance in modeling human liver physiology”.\nWhile coculture of primary human hepatocytes and Kupffer cells, human umbilical vein endothelial cells (HUVECs) and human lung fibroblasts results in the interaction of these cell types, I am not convinced that these interactions closely recapitulate interactions between the analogous cell types in the human liver in vivo. Yet, this would be required to mimic human liver physiology. Thus, I applaud the technical aspects and elegant approach that the authors have developed, but physiology is more complex and without careful attention to cell type-specific nuances, the translational merit of the model remains unclear.\nI also fully understand the practical limitations when using LSECs and how this motivates the choice of HUVECs instead. Nevertheless, HUVECs simply are fundamentally different cells that will interact with hepatocytes and immune cells in fundamentally different ways compare to LSECs. That the LSEC community is small and that integration of LSECs into a perusable network in vitro is difficult, is in my humble opinion beside the point. I am not convinced that a perfusable network of the “wrong” endothelial cells gets us closer to the emulation of human liver physiology than liver constructs perfused by artificial channels made from hydrogel."}, "author_response": "Our Response:\nWe are grateful to the reviewer for providing thoughtful additional clarifications on their perspectives. First, we appreciate their recognition of the engineering advances made in this study. We are in agreement that, while our work introduces valuable new techniques for enhanced modeling of liver diseases, there are certainly further improvements that can be made for our MPS model to more faithfully recapitulate human liver physiology. As the reviewer has pointed out, there are differences between HUVECs and human lung fibroblasts vs. their analogous cell types in the liver. Thus, the importance of various cell type-specific features must be carefully evaluated in order for this model to ultimately be useful for translational applications.\nTo address this remaining concern, we have toned down the biological claims of our study, shifting the focus to our engineering advances. We have also added more discussion on the limitations and future directions of our work regarding cell choice. The modified paragraphs from the Discussion section now read as follows, with changes in purple text:\n“In addition, this MPS could be applied to study liver-specific vascular features that accompany and contribute to metabolic dysregulation,63 using LSECs and stellate cells in the spheroids instead of HUVECs and NHLFs, and liver-derived large vessel endothelial cells for the external vasculature. Presently, incorporating these liver-specific cell types into complex in vitro models remains a major challenge across the field; primary human LSECs are notoriously difficult to properly isolate and validate,64 while standard methods for in vitro culture of stellate cells are known to result in their activation.65 While a number of groups have reported liver models that include donor LSECs or LSEC-like cells derived from stem cell populations,64–72 all of these in vitro systems lack an integrated, perfusable vasculature, ultimately limiting their translational potential. Our study seeks to narrow this gap by introducing bioengineering methods to achieve physical integration of hepatic tissue aggregates with perfusable, self-assembled microvasculature capable of exhibiting barrier integrity and signaling function.\nImportantly, the highly perfusable vasculature in our system plays a key role in facilitating our studies of monocyte recruitment. While it has been clinically established that there is increased monocyte recruitment in metabolic liver diseases such as MASLD and MASH, the timing and relationship between the recruitment of this immune population and a loss of tissue-resident Kupffer cells remains incompletely understood.39,73,74 Given that our model improves upon other vascularized liver MPS by featuring donor-matched Kupffer cells,27,28,30,75 this system is well positioned for deeper investigation of immune cell dynamics following recruitment. Having addressed the technical challenge of enabling hepatocyte-immune-microvascular interactions, our group is now able to transition toward using non-parenchymal cells (NPCs) that are either liver-specific or derived from human induced pluripotent stem cells (iPSCs).76 In particular, incorporating rigorously validated, functional iPSC-derived NPCs into these complex in vitro systems will open up new avenues for patient-specific disease modeling, allowing us to gain more physiologically relevant insights into the immune and vascular features that accompany and contribute to metabolic dysregulation in the liver.\n…\nIn conclusion, we present a liver MPS model of perfused, vascularized primary human hepatic spheroids. This platform recapitulates key metabolic and immune features of hepatic insulin resistance and establishes new methods for investigating disease-relevant monocyte infiltration. With further advances underway to incorporate liver-specific or iPSC-derived NPCs, this vascularized liver MPS holds promise for improving our ability to model complex liver disease biology and inform future therapeutic development.”"}]

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