Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment
Bibliographic record
Abstract
Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells. We cultured induced pluripotent stem cells under various conditions to induce different pluripotent states and imaged the cells using bright-field microscopy. Pluripotency states of induced pluripotent stem cells were assessed using invasive methods, including qPCR, immunostaining, flow cytometry, and RNA sequencing. Unsupervised and semi-supervised learning models were applied to evaluate the results and accurately predict the pluripotency of the cells using only image analysis. Our approach directly links images to invasive assessment results, making the analysis of cell labeling and annotation of cells in images by experts dispensable. This core achievement not only contributes for safer and more reliable stem cell research but also opens new avenues for real-time monitoring and quality control in regenerative medicine manufacturing. Our research fills an important gap in the field by providing a viable, noninvasive alternative to traditional invasive methods for assessing pluripotency. This innovation is expected to make a significant contribution to improving regenerative medicine manufacturing because it will enable a more detailed and feasible understanding of cellular status during the manufacturing process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".