Biology subjects

Patra, G.

Publications and source records attributed to Patra, G..

2 recordsLinked to original sources

bioRxiv2025

Organisation of axial regions of isolated mitotic chromosomes visualised by cryo correlative light and electron tomography

The formation of mitotic chromosomes is essential for the accurate segregation of genetic material during cell division. Increasing evidence suggests that chromosome formation involves the reorganization of DNA into loops anchored within chromosomal axial regions, whose structural organization remains insufficiently characterized. Taking advantage of DT40 cells, an avian cell model characterized by the presence of a range of chromosome sizes from 3.2-197 Mb, we have established a preparation of entire close-to-native native mitotic chromosomes for cryogenic correlative light and electron microscopy (cryo-CLEM). The size of the smallest chromosomes allows imaging of their axial regions without further thinning. Cryo-electron tomography of the chromosome axial regions reveals the presence of heterogeneous non-histone macromolecular densities (NHMDs), approximately 30-45 nm in size, interspersed within chromatin/DNA regions. We propose that NHMDs may contain condensins and contribute to chromosome architecture. In addition to NHMDs, we identified dense clusters of particles, similar in size, near the chromosome surface, likely associated with ribosomal components. To quantitatively differentiate NHMDs from these surface clusters, we developed an analytical approach based on particle interspacing and spatial distribution within the chromosome volume. By establishing a cryo-CLEM workflow for whole, near-native mitotic chromosomes, our study provides a foundation for investigating their ultrastructural architecture.

cell biology↗

bioRxiv2024

Template Learning: Deep Learning with Domain Randomization for Particle Picking in Cryo-Electron Tomography

Cryo-electron tomography (cryo-ET) enables the three-dimensional visualization of biomolecules and cellular components in their near-native state. Particle picking, a crucial step in cryo-ET data analysis, is traditionally performed by template matching--a method utilizing cross-correlations with available biomolecular templates. Despite the effectiveness of recent deep learning-based particle picking approaches, their dependence on initial data annotation datasets for supervised training remains a significant limitation. Here, we propose a technique that combines the accuracy of deep learning particle identification with the convenience of the model training on biomolecular templates enabled through a tailored domain randomization approach. Our technique, named Template Learning, automates the simulation of training datasets, incorporating considerations for molecular crowding, structural variabilities, and data acquisition variations. This reduces or even eliminates the dependence of supervised deep learning on annotated experimental datasets. We demonstrate that models trained on simulated datasets, optionally fine-tuned on experimental datasets, outperform those exclusively trained on experimental datasets. Also, we illustrate that Template Learning used as an alternative to template matching, can offer higher precision and better orientational isotropy, especially for picking small non-spherical particles. Template Learning software is open-source, Python-based, and GPU and CPU parallelized.

molecular biology↗