Biology subjects

Becker, T. M.

Publications and source records attributed to Becker, T. M..

3 recordsLinked to original sources

bioRxiv2025

Deep multiplexed 50-marker imaging of circulating tumor cells expands actionable biomarker profiling for precision oncology

Liquid biopsy-derived circulating tumor cells (CTCs) offer a minimally invasive avenue for precision oncology by enabling longitudinal monitoring of actionable molecular and cell-phenotypic biomarkers. However, conventional immunofluorescence imaging-based CTC profiling captures just 4-5 markers per cell, restricting crucial insights into oncogenic and resistance drivers, as well as tumor cell heterogeneity. Here we present an integrated pipeline employing deep multiplexed imaging to profile up to 50 molecular markers per CTC, capturing expression, phosphorylation, and subcellular localization of diverse biomarkers. Validated in a multi-stage prostate cancer resistance model and applied to prostate cancer patient-derived CTCs, this approach enhances CTC classification and reveals inter- and intra-patient heterogeneity correlating with therapy response. Machine learning identified therapeutically actionable signatures, suggesting patient-specific treatment strategies. This deep multiplexed imaging pipeline advances the utility of CTCs in guiding personalized cancer therapy by providing comprehensive molecular and phenotypic insights through minimally invasive liquid biopsies. Highlights- Multiplexed immunofluorescence imaging of circulating tumor cells captures up to 50 molecular markers per cell with subcellular localisation. - Single-cell and subcellular analyses identify quantitative signatures correlating with patient outcome, as well as therapeutically actionable biomarkers. - This method transforms the utility of CTCs for future use in patient stratification and for guiding personalised therapies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/684075v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@a36ff4org.highwire.dtl.DTLVardef@b06c1org.highwire.dtl.DTLVardef@1e8a572org.highwire.dtl.DTLVardef@c60753_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

cancer biology↗

bioRxiv2025

Separation slang - Laboratory mice use low-frequency call repertoire during physical separation

The discovery of a diverse repertoire of ultrasonic vocalizations (USVs) sparked interest in understanding their role in mouse social behavior. Social communication in mice is not just vocal, but multimodal and occurs mostly in close proximity. Aiming to unravel the impact direct physical interaction has on the vocal communication of same-sex mouse dyads, we separated mice through a divider preventing direct physical interaction, but allowing visual, olfactory and some tactile interaction through holes. Separated dyads emitted a distinct call repertoire consisting mainly of calls in or just above the human audible range (but not squeaks) as well as Noisy calls, and only to a lesser degree of USVs. Increasing the possibility for direct interaction through larger holes in the divider led to an adaption of the call repertoire. The separation-induced call repertoire was neither affected by sex, nor was it mouse strain specific, even though differences in spectro-temporal parameters and call class proportion occurred. Lastly, buspirone treatment showed no observable effect, suggesting anxiety to not be the main driver underlying the separation-induced call repertoire. We show that separated same-sex mouse dyads predominantly emit a call repertoire that until now has only been observed in isolation or during aversive stimulation.

animal behavior and cognition↗

bioRxiv2023

CRUX, a platform for visualising, exploring and analysing cancer genome cohort data

To better understand how tumours develop, identify prognostic biomarkers, and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (1) it has an easy-to-use graphical interface; (2) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (3) it allows analyses to be run locally to address data privacy concerns (though an online version is also available); and (4) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUXs capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).

bioinformatics↗