Biology subjects

Madsen, T.

Publications and source records attributed to Madsen, T..

4 recordsLinked to original sources

bioRxiv2026

Spatially Distinct Myosin II Architectures Regulate Protrusion Dynamics and Directional Persistence during Immune Cell Migration

Directional persistence is essential for efficient immune cell migration in tissues, yet how cytoskeletal systems stabilize migration in complex three-dimensional environments remains unclear. Using intravital subcellular microscopy and quantitative analysis of membrane dynamics, we identify two spatially distinct architectures of non-muscle myosin II (NMII) that coordinate protrusion dynamics during neutrophil migration. In vivo and in collagen matrices, NMII assembles at the leading edge into lattice-like structures that are structurally and functionally distinct from rear contractile actomyosin bundles. Protrusion-resolved analyses reveal that directional persistence correlates strongly with protrusion lifetime and sustained NMII engagement, with rear NMII load showing the strongest association with protrusion persistence. Strikingly, directional migration is not determined by the abundance of favorable protrusions but by their temporal organization during migration. Pharmacological perturbations that redistribute NMII activity disrupt this temporal organization and alter migration trajectories. Together, these findings reveal that spatially distinct NMII architectures coordinate protrusion dynamics across time to stabilize directional migration in complex environments. HighlightsO_LIMigrating neutrophils assemble spatially distinct myosin II architectures at the leading edge and rear C_LIO_LIProtrusion dynamics and directional persistence are linked to sustained myosin II engagement C_LIO_LIDirectional migration emerges from the temporal organization of protrusion states rather than their abundance C_LI

cell biology↗

bioRxiv2023

Fusion-Negative Rhabdomyosarcoma xenografts in murine tongue provide a model for the study of local invasion, intravasation and metastasis at cellular and subcellular levels in live animals

PAX3/7 Fusion-negative rhabdomyosarcoma (FN-RMS) is a childhood mesodermal lineage malignancy with a poor prognosis for metastatic or relapsed cases. Towards achieving a more complete understanding of advanced FN-RMS, we developed an orthotopic tongue xenograft model for studies of molecular basis of FN-RMS invasion and metastasis. The behavior of FN-RMS cells injected into murine tongue was examined using in vivo bioluminescence imaging, non-invasive intravital microscopy (IVM), and histopathology and compared to the prevailing hindlimb intramuscular and subcutaneous xenografts. FN-RMS cells were retained in the tongue and invaded locally into muscle mysial spaces and vascular lumen. While evidence of hematogenous dissemination to the lungs occurred in tongue and intramuscular xenografts, evidence of local invasion and lymphatic dissemination to lymph nodes only occurred in tongue xenografts. IVM and RNA-seq of tongue xenografts reveal shifts in cellular phenotype and differentiation state in tongue xenografts. IVM also shows homing to blood and lymphatic vessels, lymphatic intravasation, and dynamic membrane protrusions. Based on these findings, the tongue orthotopic xenograft of FN-RMS is a valuable model for tumor progression studies at the tissue, cellular and subcellular levels providing insight into kinetics and molecular bases of tumor invasion and metastasis and, hence, new therapeutic avenues for advanced FN-RMS.

cancer biology↗

bioRxiv2018

EBADIMEX: An empirical Bayes approach to detect joint differential expression and methylation and to classify samples

1DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types.\n\nHere, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets.\n\nEBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

bioinformatics↗

bioRxiv2017

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Non-coding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g. TERT and CDH10) and survival (e.g. CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217 and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

cancer biology↗