Single cell functional analysis of multiple myeloma cell populations correlates with diffusion profiles in static microfluidic coculture systems
Bibliographic record
Abstract
Microfluidic cell culture systems are becoming increasingly useful for studying biology questions, particularly those involving small cell populations that are cultured within microscale geometries mimicking the complex cellular microenvironment. Depending on the geometry and spatial organization of these cell populations, however, paracrine signaling between cell types can depend critically on spatial concentration profiles of soluble factors generated by diffusive transport. In scenarios where single cell data are acquired to study cell population heterogeneities in functional response, uncertainty associated with concentration profiles can lead to interpretation bias. To address this issue and provide important evidence on how diffusion develops within typical microfluidic cell culture systems, a combination of experimental and computational approaches were applied to measure and predict concentration patterns within microfluidic geometries, and characterize the functional response of culture cells based on single-cell resolution transcription factor activation. Using a model coculture system consisting of multiple myeloma cells (MMCs) and neighboring bone marrow stromal cells (BMSCs), we measured concentrations of three cytokines (IL-6, VEGF, and TNF-α) in conditioned media collected from separate culture compartments using a multiplex ELISA system. A 3D numerical model was developed to predict biomolecular diffusion and resulting concentration profiles within the tested microsystems and compared with experimental diffusion of 20 kDa FITC-Dextran. Finally, diffusion was further characterized by controlling exogenous IL-6 diffusion and the coculture spatial configuration of BMSCs to stimulate STAT3 nuclear translocation in MMCs. Results showed agreement between numerical and experimental results, provided evidence of a shallow concentration gradient across the center well of the microsystem that did not lead to a bias in results, and demonstrated that microfluidic systems can be tailored with specific geometries to avoid spatial bias when desired.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".