Biology subjects

Roskelley, C.

Publications and source records attributed to Roskelley, C..

4 recordsLinked to original sources

bioRxiv2026

A genome-wide CRISPR screen reveals cancer-specific regulators of hyaluronan binding and cellular invasion

Metastatic spread of cancer cells is driven by binding between the cell-surface receptor CD44 and hyaluronan (HA) in the extracellular matrix. The specific oncogenes that drive increased CD44-HA binding in cancer remain poorly defined. Using a fluorescently labeled hyaluronan probe, we performed a genome-wide CRISPR screen to identify genes whose knockdown disrupts HA binding in breast cancer cells. We subsequently developed a bioinformatic analysis pipeline that enabled stratification and prioritization of cancer-specific regulators. This work provides a first-in-class resource for the identification of druggable targets to inhibit HA binding. We further validate RAB4A, a top hit from our screen, as a novel regulator of this process. Mechanistically, RAB4A KO dramatically reduces CD44 expression and inhibits the invasion of breast cancer cells through HA-rich matrices. This study validates a novel strategy for identifying regulators of cancer cell invasion and identifies immediate actionable targets for anti-metastatic therapy.

cancer biology↗

bioRxiv2024

PAN deadenylase is required for mitosis in response to microtubule stress in yeast and humans

HIGHLIGHTSO_LIPAN2-PAN3 catalytic activity is required for mitotic robustness under MT stress C_LIO_LICatalytic-dead Pan2-D1020A phenocopies pan2{Delta}: spindle defects and G2/M arrest C_LIO_LILoss of Pan2 catalytic activity amplifies nocodazole-induced changes in CLB1 and CLN3 mRNA C_LIO_LIPAN2 depletion in human cells produces multipolar spindles under MT stress C_LI IN BRIEFVerma et al. show that PAN2-PAN3 deadenylase catalytic activity is selectively required for mitotic fidelity under microtubule stress. Yeast pan2{Delta} cells exhibit spindle defects, G2/M arrest, and altered CLB1 and CLN3 mRNA levels under nocodazole. PAN2 knockdown in human cells produces multipolar spindles, demonstrating evolutionary conservation. Cytoplasmic deadenylation by PAN2-PAN3 and CCR4-NOT regulates mRNA stability and translation, but the contribution of individual deadenylases to mitotic progression is poorly defined. Here we show that PAN2-PAN3 catalytic activity is selectively required for mitotic robustness under microtubule stress. In budding yeast, the catalytically inactive Pan2-D1020A mutant fails to complement pan2{Delta} nocodazole sensitivity, leading to G2/M arrest, spindle defects, and increased cell death. pan2{Delta} exhibits synthetic genetic interactions with tubulin and tubulin-folding genes, and RNA-seq reveals dysregulation of cell-cycle transcripts including the cyclins CLB1 and CLN3. The phenotype is stress-conditional: pan2{Delta} cells proliferate normally without drug. In mammalian cells, PAN2 depletion sensitizes HEK293A cells to nocodazole and colchicine and produces prolonged metaphase with multipolar spindles. Together, these findings define a conserved, stress-conditional role for PAN2-PAN3 deadenylase activity in safeguarding mitotic fidelity when microtubules are compromised. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/591760v3_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@19e6662org.highwire.dtl.DTLVardef@53ba18org.highwire.dtl.DTLVardef@de9c99org.highwire.dtl.DTLVardef@fb0f9d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO See accompanying graphical abstract file. C_FIG

cell biology↗

bioRxiv2021

Epithelial-mesenchymal plasticity induced by discontinuous exposure to TGFβ1 promotes tumour growth

Transitions between epithelial and mesenchymal cellular states (EMT/MET) contribute to cancer progression. We hypothesize that EMT followed by MET promotes cell population heterogeneity favouring tumour growth. We developed an EMT model by on/off exposure of epithelial EpH4 cells (E-cells) to TGF{beta}1 that mimics phenotypic EMT (M-cells) and MET. We aimed at understanding whether phenotypic MET is accompanied by molecular and functional reversion back to epithelia, by using RNA sequencing, Immunofluorescence (IF), proliferation, wound healing, focus formation and mamosphere formation assays, as well as cell-xenografts in nude mice. Phenotypic reverted-epithelial cells (RE-cells), obtained after MET induction, presented pure epithelial morphology and proliferation rate resembling E-cells. However, RE transcriptomic profile and IF staining of epithelial and mesenchymal markers revealed a unique and heterogeneous mixture of cell-subpopulations, with high self-renewal ability fed by oxidative phosporylation. RE-cells heterogeneity is stably maintained for long periods after TGF{beta}1 removal, both in vitro and in large derived tumours in nude mice. Overall, we show that phenotypic reverted-epithelial cells (RE-cells) do not return to the molecular and functional epithelial state, present mesenchymal features related with aggressiveness and cellular heterogeneity that favour tumour growth in vivo. This work strengthens epithelial cells reprogramming and cellular heterogeneity fostered by inflammatory cues as a tumour-growth promoting factor in vivo.

cell biology↗

bioRxiv2019

A methodology for morphological feature extraction and unsupervised cell classification

Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition, including images and movies of cells, computer-assisted identification and analysis of images becomes essential. Here, we report on techniques for recognition of cells in microscopic images and automated cell shape classification. We illustrate how our unsupervised machine-learning-based approach can be used to classify distinct cell shapes from a large number of microscopic images.\n\nTechnical AbstractWe develop a methodology to segment cells from microscopy images and compute quantitative descriptors that characterize their morphology. Using unsupervised techniques for dimensionality reduction and density-based clustering, we perform label-free cell shape classification. Cells are identified with minimal user input using mathematical morphology and region-growing segmentation methods. Physical quantities describing cell shape and size (including area, perimeter, Feret diameters, etc.) are computed along with other features including shape factors and Hus image moments.\n\nCorrelated features are combined to obtain a low-dimensional (2-D or 3-D) embedding of data points corresponding to individual segmented cell shapes. Finally, a hierarchical density-based clustering algorithm (HDBSCAN) is used to classify cells. We compare cell classification results obtained from different combinations of features to identify a feature set that delivers optimum classification performance for our test data consisting of phase-contrast microscopy images of a pancreatic-cancer cell line, MIA PaCa-2.

bioinformatics↗