Alterations of centromere positions in nuclei of immortalized and malignant mouse lymphocytes
Bibliographic record
Abstract
BACKGROUND: The three-dimensional (3D) positions of centromeres have been studied in several cell systems. However, data on centromere positions during cellular transformation remain elusive. This study has focused on mouse lymphocytes and investigated the centromere positions in primary, immortalized, and tumor cells. METHODS: Eighty-to-ninety z-slices of each mouse lymphocyte nucleus were acquired using a sampling distance of 107 nm in the xy plane and 200 nm along z for each z-stack, using an Axioplan 2 microscope, an AxioCam HR CCD, a 63x/1.4 oil objective, and the Axiovision 3.1 software (Carl Zeiss, Canada). A constrained iterative algorithm (Schaefer et al., J Microsc 2001;204:99-107) was used for deconvolution. Centromere positions in 3D images were analyzed using CentroView, a program we developed to measure nuclear centromere positions. RESULTS: Using CentroView we determined the positions of centromeres in primary lymphocytes, immortalized and malignant mouse B cells. We show that centromeres exhibit altered nuclear positions in immortalized and malignant B cells. These changes are independent of previously described cell cycle-dependent centromere dynamics. CONCLUSIONS: The 3D positions of centromeres are altered during cellular transformation. In lymphocytes, centromeres are found in more central nuclear positions following immortalization and transformation. These nuclear changes reflect structural remodeling of mammalian nuclei during oncogenesis and may impact on the structural organization of chromosomes. How centromeric changes are linked to nuclear remodeling can now be quantitatively examined using the tools of this study.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".