The potential for complete automated scoring of the cytokinesis block micronucleus cytome assay using imaging flow cytometry
Bibliographic record
Abstract
The lymphocyte Cytokinesis-Block Micronucleus (CBMN) assay was originally developed for the measurement of micronuclei (MN) exclusively in binucleated (BN) cells, which represent the population of cells that can express MN because they completed nuclear division. Recently the assay has evolved into a comprehensive cytome method to include biomarkers that measure chromosomal instability and cytotoxicity by quantification of nuclear buds (NBUDs), nucleoplasmic bridges (NPBs) and apoptotic/necrotic cells. Furthermore, enumeration of mono- and polynucleated cells allows for computation of the nuclear division index (NDI) to assess mitotic activity. Typically performed by manual microscopy, the CBMN cytome assay is laborious and subject to scorer bias and fatigue, leading to inter- and intra-scorer variability. Automated microscopy and conventional flow cytometry methods have been developed to automate scoring of the traditional and cytome versions of the assay. However, these methods have several limitations including the requirement to create high-quality microscope slides, lack of staining consistency and sub-optimal nuclear/cytoplasmic visualization. In the case of flow cytometry, stripping of the cytoplasmic membrane makes it impossible to measure MN in BN cells, calculate the NDI or to quantify apoptotic or necrotic cells. Moreover, the absence of cellular visualization using conventional flow cytometry, makes it impossible to quantify NBUDs and NPBs. In this review, we propose that imaging flow cytometry (IFC), which combines high resolution microscopy with flow cytometry, may overcome these limitations. We demonstrate that by using IFC, images from cells in suspension can be captured, removing the need for microscope slides and allowing visualization of intact cytoplasmic membranes and DNA content. Thus, mono-, bi- and polynucleated cells with and without MN can be rapidly and automatically identified and quantified. Finally, we present high-resolution cell images containing NBUDs and NPBs, illustrating that IFC possesses the potential for completely automated scoring of all components of the CBMN cytome assay.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".