Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns
A prominent feature of eukaryotic chromosomes are centromeres, which are specialized regions of repetitive DNA required for faithful chromosome segregation during cell division. In interphase cells, centromeres are non-randomly positioned in the three-dimensional space of the nucleus in a cell type-...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Cells |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4409/14/7/491 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850188245254537216 |
|---|---|
| author | Adib Keikhosravi Krishnendu Guin Gianluca Pegoraro Tom Misteli |
| author_facet | Adib Keikhosravi Krishnendu Guin Gianluca Pegoraro Tom Misteli |
| author_sort | Adib Keikhosravi |
| collection | DOAJ |
| description | A prominent feature of eukaryotic chromosomes are centromeres, which are specialized regions of repetitive DNA required for faithful chromosome segregation during cell division. In interphase cells, centromeres are non-randomly positioned in the three-dimensional space of the nucleus in a cell type-specific manner. The functional relevance and the cellular mechanisms underlying this localization are unknown, and quantitative methods to measure distribution patterns of centromeres in 3D space are needed. Here, we developed an analytical framework that combines sensitive clustering metrics and advanced modeling techniques for the quantitative analysis of centromere distributions at the single-cell level. To identify a robust quantitative measure for centromere clustering, we benchmarked six metrics for their ability to sensitively detect changes in centromere distribution patterns from high-throughput imaging data of human cells, both under normal conditions and upon experimental perturbation of centromere distribution. We found that Ripley’s K function has the highest accuracy with minimal sensitivity to variations in the number of centromeres, making it the most suitable metric for measuring centromere distributions. As a complementary approach, we also developed and validated spatial models to replicate centromere distribution patterns, and we show that a radially shifted Gaussian distribution best represents the centromere patterns seen in human cells. Our approach creates tools for the quantitative characterization of spatial centromere distributions with applications in both targeted studies of centromere organization and unbiased screening approaches. |
| format | Article |
| id | doaj-art-5edb55ccd4434babbafd1f22ab898c65 |
| institution | OA Journals |
| issn | 2073-4409 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj-art-5edb55ccd4434babbafd1f22ab898c652025-08-20T02:15:55ZengMDPI AGCells2073-44092025-03-0114749110.3390/cells14070491Simulation and Quantitative Analysis of Spatial Centromere Distribution PatternsAdib Keikhosravi0Krishnendu Guin1Gianluca Pegoraro2Tom Misteli3High Throughput Imaging Facility (HiTIF), National Cancer Institute, NIH, Bethesda, MD 20892, USACell Biology of Genomes Group, National Cancer Institute, NIH, Bethesda, MD 20892, USAHigh Throughput Imaging Facility (HiTIF), National Cancer Institute, NIH, Bethesda, MD 20892, USACell Biology of Genomes Group, National Cancer Institute, NIH, Bethesda, MD 20892, USAA prominent feature of eukaryotic chromosomes are centromeres, which are specialized regions of repetitive DNA required for faithful chromosome segregation during cell division. In interphase cells, centromeres are non-randomly positioned in the three-dimensional space of the nucleus in a cell type-specific manner. The functional relevance and the cellular mechanisms underlying this localization are unknown, and quantitative methods to measure distribution patterns of centromeres in 3D space are needed. Here, we developed an analytical framework that combines sensitive clustering metrics and advanced modeling techniques for the quantitative analysis of centromere distributions at the single-cell level. To identify a robust quantitative measure for centromere clustering, we benchmarked six metrics for their ability to sensitively detect changes in centromere distribution patterns from high-throughput imaging data of human cells, both under normal conditions and upon experimental perturbation of centromere distribution. We found that Ripley’s K function has the highest accuracy with minimal sensitivity to variations in the number of centromeres, making it the most suitable metric for measuring centromere distributions. As a complementary approach, we also developed and validated spatial models to replicate centromere distribution patterns, and we show that a radially shifted Gaussian distribution best represents the centromere patterns seen in human cells. Our approach creates tools for the quantitative characterization of spatial centromere distributions with applications in both targeted studies of centromere organization and unbiased screening approaches.https://www.mdpi.com/2073-4409/14/7/491centromerespatial distributionclustering metricsimage analysisRipley’s Kgenome organization |
| spellingShingle | Adib Keikhosravi Krishnendu Guin Gianluca Pegoraro Tom Misteli Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns Cells centromere spatial distribution clustering metrics image analysis Ripley’s K genome organization |
| title | Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns |
| title_full | Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns |
| title_fullStr | Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns |
| title_full_unstemmed | Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns |
| title_short | Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns |
| title_sort | simulation and quantitative analysis of spatial centromere distribution patterns |
| topic | centromere spatial distribution clustering metrics image analysis Ripley’s K genome organization |
| url | https://www.mdpi.com/2073-4409/14/7/491 |
| work_keys_str_mv | AT adibkeikhosravi simulationandquantitativeanalysisofspatialcentromeredistributionpatterns AT krishnenduguin simulationandquantitativeanalysisofspatialcentromeredistributionpatterns AT gianlucapegoraro simulationandquantitativeanalysisofspatialcentromeredistributionpatterns AT tommisteli simulationandquantitativeanalysisofspatialcentromeredistributionpatterns |