True monolayer cell culture in a confined 3D microenvironment enables lineage informatics
Bibliographic record
Abstract
BACKGROUND: There is a need for methods to (1) track cells continuously to generate lineage trees; (2) culture cells in in vivo-like microenvironments; and (3) measure many biological parameters simultaneously and noninvasively. Herein, we present a novel imaging culture chamber that facilitates "lineage informatics," a lineage-centric approach to cytomics. METHODS: We cultured cells in a confined monolayer using a novel "gap chamber" that produces images with confocal-like qualities using standard DIC microscopy. Lineage and other cytometric data were semiautomatically extracted from image sets of neural stem and progenitor cells and analyzed using lineage informatics. RESULTS: Cells imaged in the chamber every 3 min could be tracked for at least 6 generations allowing for the construction of extensive lineage trees with multiparameter data sets at hundreds of time points for each cell. The lineage informatics approach reveals relationships between lineage, phenotype, and microenvironment. Mass transfer characteristics and 3D geometry make the chamber more in vivo-like than traditional culture systems. CONCLUSIONS: The gap chamber allows cells to be cultured, imaged, and tracked in true monolayers permitting detailed informatics analysis of cell lineage, phenotype, and fate determinants. The chamber is biomimetic and straightforward to build and use, and should find many applications in long-term cell imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".