Insulin‐dependent glucose consumption dynamics in 3D primary human liver cultures measured by a sensitive and specific glucose sensor with nanoliter input volume
Bibliographic record
Abstract
Abstract The liver plays a central role in glucose homeostasis and hepatic insulin resistance constitutes a key feature of type 2 diabetes. However, platforms that accurately mimic human hepatic glucose disposition and allow for rapid and scalable quantification of glucose consumption dynamics are lacking. Here, we developed and optimized a colorimetric glucose assay based on the glucose oxidase‐peroxidase system and demonstrate that the system can monitor glucose consumption in 3D primary human liver cell cultures over multiple days. The system was highly sensitive (limit of detection of 3.5 µM) and exceptionally accurate (R 2 = 0.999) while requiring only nanoliter input volumes (250 nL), enabling longitudinal profiling of individual liver microtissues. By utilizing a novel polymer, off‐stoichiometric thiol‐ene (OSTE), and click‐chemistry based on thiol‐Michael additions, we furthermore show that the assay can be covalently bound to custom‐build chips, facilitating the integration of the sensor into microfluidic devices. Using this system, we find that glucose uptake of our 3D human liver cultures closely resembles human hepatic glucose uptake in vivo as measured by euglycemic‐hyperinsulinemic clamp. By comparing isogenic insulin‐resistant and insulin‐sensitive liver cultures we furthermore show that insulin and extracellular glucose levels account for 55% and 45% of hepatic glucose consumption, respectively. In conclusion, the presented data show that the integration of accurate and scalable nanoliter glucose sensors with physiologically relevant organotypic human liver models enables longitudinal profiling of hepatic glucose consumption dynamics that will facilitate studies into the biology and pathobiology of glycemic control, as well as antidiabetic drug screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".