From genotype to phenotype: decoding mutations in blasts by holo-tomographic flow cytometry
Bibliographic record
Abstract
Cup-like nuclear morphological alterations in acute myeloid leukemia (AML) blasts have been widely correlated with Nucleophosmin 1 (NPM1) mutations. NPM1-mutated AML has earned recognition as a distinct entity among myeloid tumors, but the absence of a thoroughly established tool for its morphological analysis remains a notable gap. Holographic tomography (HT) can offer a label-free solution for quantitatively assessing the 3D shape of the nucleus based on the volumetric variations of its refractive indices (RIs). However, traditional HT methods analyze adherent cells in a 2D layer, leading to non-isotropic reconstructions due to missing cone artifacts. Here we show for the first time that holo-tomographic flow cytometry (HTFC) achieves quantitative specificity and precise capture of the nucleus volumetric shape in AML cells in suspension. To retrieve nucleus specificity in label-free RI tomograms of flowing AML cells, we conceive and demonstrate in a real-world clinical case a novel strategy for segmenting 3D concave nuclei. This method implies that the correlation between the "phenotype" and "genotype" of nuclei is demonstrated through HTFC by creating a challenging link not yet explored between the aberrant morphological features of AML nuclei and NPM1 mutations. We conduct an ensemble-level statistical characterization of NPM1-wild type and NPM1-mutated blasts to discern their complex morphological and biophysical variances. Our findings suggest that characterizing cup-like nuclei in NPM1-related AML cells by HTFC may enhance the diagnostic approach for these tumors. Furthermore, we integrate virtual reality to provide an immersive fruition of morphological changes in AML cells within a true 3D environment.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".