Abstract 346: A Standard Flow Cytometry Protocol for Assessing Human Pluripotent Stem Cell-derived Cardiomyocyte Identity by Troponin Positivity
Bibliographic record
Abstract
Directed differentiation of human pluripotent stem cells (hPSC) into cardiomyocytes (hPSC-CM) offers an inexhaustible supply of cells for basic science research and translational applications. However, despite significant advancements in defining the factors most critical for differentiation, the resulting cultures remain a heterogeneous mixture of cells. Ultimately, as heterogeneity can pose challenges to interpreting functional data, the ability to accurately and precisely assess cell identity in differentiation cultures is paramount to well-defined and reproducible studies. To date, a standardized flow cytometry protocol that is broadly accepted among laboratories has not been established for assessing cell type heterogeneity within hPSC-CM cultures, posing challenges to evaluating outcomes generated among laboratories and studies. A survey of studies published over the past seven years (1/2010-10/2017) reveals the wide range of antibodies and experimental conditions reported for flow cytometry-based assessment of hPSC-CM. Although our literature survey revealed that a preponderance of studies relied on TNNT2 as a marker of cardiomyocyte identity, TNNI3 is more specific to cardiomyocytes than TNNT2 throughout development. We applied targeted mass spectrometry to confirm the presence of TNNI3 in our hPSC-CM. We then investigated five commercially available anti-TNNI3 and three sample preparation techniques for use in the assessment of heterogeneity of hPSC-CM cultures by flow cytometry. Results demonstrate two of the five anti-TNNI3 clones appear suitable for marking hPSC-CM and reveal differential susceptibility of antibody clones to sample preparation conditions. To further test the rigor and utility of our flow cytometry protocol, it was shared with two collaborating laboratories and applied to heterogeneous mixtures of positive and negative cell types. Results from these two laboratories demonstrate the protocol successfully distinguishes positive and negative cell types similarly, despite using different cell lines and differentiation protocols. We propose a workflow for establishing the fit-for-purpose use of antibodies and a standard protocol for assessing hPSC-CM identity by troponin positivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 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".