Biology subjects

Shivashankar, G.

Publications and source records attributed to Shivashankar, G..

4 recordsLinked to original sources

bioRxiv2025

Acute chromatin decompaction stiffens the nucleus as revealed by nanopillar-induced nuclear deformation in cells

Chromatin architecture is critical in determining nuclear mechanics. Most studies focus on the mechanical rigidity conferred by chromatin compaction from densely packed heterochromatin, but less is known on how transient changes in chromatin decompaction state impinge on nucleus stiffness. Here, we used an array of vertically aligned nanopillars to study nuclear deformability in situ after chromatin decompaction in cells. The nucleus significantly stiffened within 4 hours of chromatin decompaction but softened at longer timescales. This acute nucleus stiffening was predominantly underlied by an increase in nucleus volume, nuclear import and partially enhanced by lamin protein recruitment to the nuclear periphery. The coupling between nucleus stiffening and acute chromatin decompaction was observed in cancer cell lines with lower malignancy (e.g. MCF7, PEO1, A549) but weakened in those with higher metastatic potential (e.g. MDA-MB-231, HEYA8, HT1080), which was found to be associated with the capacity to efficiently compact heterochromatin into foci that sustains nucleus deformability required for confined migration. Our work signals how a rapid chromatin remodeling is a physiologically relevant pathway to modulate nucleus mechanics and cell migration behavior. STATEMENT OF SIGNIFICANCEMany cell processes such as wound healing, immune activation and DNA damage repair require a decompact and accessible chromatin structure. Whether such short-term remodeling of the chromatin impacts nucleus mechanics and function is poorly defined. Using nanopillars that allow interrogation of nucleus rigidity within intact cells, we showed that contrary to conventional knowledge the nucleus becomes less deformable and more rigid when chromatin is acutely decompacted due to enhanced nuclear import and swelling of the nucleus. In cancer cells, the coupling of transient chromatin decompaction to nucleus rigidity is weakened and appears to be countered by heterochromatin formation and compaction. We demonstrate here how short-term chromatin remodeling can impact nucleus and cellular properties in a time-dependent and non-genetic manner.

bioengineering↗

bioRxiv2024

Partially Shared Multi-Modal Embedding Learns Holistic Representation of Cell State

Experimental technologies for jointly measuring different data modalities at the single-cell level offer different windows into cell state. To obtain a holistic understanding of cell state, computational methods are needed that carefully integrate the different views to capture shared information as well as tease apart modality-specific information. We present a computational framework that automatically learns partial information sharing between multiple modalities by using an Autoencoder with a Partially Overlapping Latent space learned through Latent Optimization (APOLLO). On paired scRNA-seq and scATAC-seq data (SHARE-seq) and paired scRNA-seq and surface protein data (CITE-seq), we demonstrate that APOLLO comprehensively and automatically identifies and distinguishes between information captured by both modalities, in the shared latent space, and modality-specific information. Beyond sequencing modalities, large-scale multiplexed single-cell imaging datasets, such as the Human Protein Atlas, are becoming available that allow studying how protein localization relates to function. While chromatin, microtubules or ER are standardly stained as a reference, little is known about the information shared between these stains. We found that APOLLO enables the prediction of missing modalities, such as unmeasured protein stains, and allows disentangling which modality or cellular compartment is linked with a specific phenotype, such as the variability in protein localization observed across single cells. Collectively, APOLLO enables explicit learning of shared and modality-specific information leading to a more holistic understanding of cell state and the underlying regulatory mechanisms. APOLLO is a general framework that can be applied to any multi-modal data well beyond the single-cell domain including, for example, large-scale medical biobanks.

cell biology↗

bioRxiv2019

Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders

The development of single-cell methods for capturing different data modalities including imaging and sequencing have revolutionized our ability to identify heterogeneous cell states. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct sub-populations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

bioinformatics↗

bioRxiv2018

Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes

Although we can increasingly image and measure biological processes at single-cell resolution, most assays can only take snapshots from a population of cells in time. Here we describe ImageAEOT, which combines an AutoEncoder, to map single-cell Images from different cell populations to a common latent space, with the framework of Optimal Transport to infer cellular trajectories. As a proof-of-concept, we apply ImageAEOT to nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Importantly, ImageAEOT can infer the trajectory of a particular cell from one snapshot in time and identify the changing features to provide early biomarkers for developmental and disease progression.

bioinformatics↗