Biology subjects

Chaudhry, G.

Publications and source records attributed to Chaudhry, G..

3 recordsLinked to original sources

bioRxiv2024

Genomic and epigenomic maps of mouse centromeres and pericentromeres

Satellite DNA makes up [~]11% of the mouse genome, predominantly located in centromeric and pericentric regions, which are crucial for chromosome segregation. While comprehensive assemblies of these regions have been established in the human genome, they are still lacking in the mouse genome. In this study, we used PacBio long-read sequencing, CUT&RUN sequencing, DNA methylation analysis, and RNA sequencing to generate genomic and epigenomic maps of these regions. We find that centromeric regions are primarily occupied by 120-mer Minor satellites, with other Minor Satellite length variants, 112-mers and 112-64-dimers, localized at centromere-pericentric junctions. Pericentromeric regions are mainly composed of homogeneous Major satellites, while pericentric-chromosomal junctions contain a higher density of divergent satellites. Additionally, the density of non-satellite repeats increases progressively from centromeres to pericentromeres, and further toward chromosomal arm junctions. We found that 120-mer Minor satellites in the core centromere are highly enriched with CENP-A, while the 112-mers and 112-64-dimers show lower CENP-A levels. Homogeneous Major satellites are more enriched with H3K9me3 heterochromatin, whereas divergent Major satellites are preferentially associated with H3K27me3. Furthermore, DNA methylation levels are lower in centromeres compared to pericentric regions. We also observed that only a small subset of satellites is transcribed into RNA, particularly regions exhibiting lower DNA methylation density. Our comprehensive assembly and characterization of the genomic and epigenomic landscape of mouse centromeric and pericentric regions have major implications for satellite biology and ongoing mouse telomere-to-telomere (T2T) assembly efforts.

genomics↗

bioRxiv2022

Molecular machines stimulate intercellular calcium waves and cause muscle contraction

Intercellular calcium waves (ICW) are complex signaling phenomena that control many essential biological activities, including smooth muscle contraction, vesicle secretion, gene expression, and changes in neuronal excitability. Accordingly, the remote stimulation of ICW may result in versatile new biomodulation and therapeutic strategies. Here, we demonstrate that light-activated molecular machines (MM), molecules that rotate and perform mechanical work on the molecular scale, can remotely stimulate ICW. Live-cell calcium tracking and pharmacological experiments reveal that MM-induced ICW are driven by the activation of inositol triphosphate (IP3) mediated signaling pathways by unidirectional, fast-rotating MM. We then demonstrated that MM-induced ICW can be used to control muscle contraction in vitro in cardiomyocytes and animal behavior in vivo in Hydra vulgaris. Consequentially, this work demonstrates a new strategy for the direct control of cell signaling and downstream biological function using molecular-scale devices.

bioengineering↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗