Biology subjects

Rieck, B. A.

Publications and source records attributed to Rieck, B. A..

3 recordsLinked to original sources

bioRxiv2022

Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification

MotivationPredicting antimicrobial resistance using MALDI-TOF mass spectrometry based machine learning is a fast-growing field of research. Recent advances in machine learning methods specifically designed for MALDI-TOF mass spectra have outperformed established classification approaches. However, classification performance was observed to have a large standard deviation between different train-test splits. We hypothesise that this variance is caused by the underlying phylogenetic structure between microbial samples, which is implicitly reflected in their MALDI-TOF MS profiles, but not taken into account during the training of a model. ResultsIn this paper, we propose to infer this structure from the dataset--using agglomerative hierarchical clustering--and consider it during the dataset splitting between train and test. We show that incorporating such phylogenetic structure into the antimicrobial resistance prediction scenario leads to an improved classification performance. Average precision was increased from 42.3 to 47.1 for ciprofloxacin resistance prediction in Escherichia coli and from 44.6 to 50.8 for amoxicillin-clavulanic acid resistance prediction in Staphylococcus aureus using a Gaussian process classifier with a MALDI-TOF MS specific kernel. We envision that these results will support the quick and reliable identification of antimicrobial resistances, thus increasing patient well-being and reducing healthcare costs. AvailabilityAll data is available for download and code available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE at branch maldi_stratification. Contactcaroline.weis@bsse.ethz.ch, karsten.borgwardt@bsse.ethz.ch Supplementary informationSupplementary information at the end of document.

bioinformatics↗

bioRxiv2021

Tissue-wide coordination of calcium signaling regulates the epithelial stem cell pool during homeostasis

Skin epidermal homeostasis is maintained via constant regeneration by stem cells, which must communicate to balance their self-renewal and differentiation. A key molecular pathway, Ca2+ signaling has been implicated as a signal integrator in developing and wounded epithelial tissues[1, 2, 3, 4]. Yet how stem cells carry out this signaling across a regenerative tissue remains unknown due to significant challenges in studying signaling dynamics in live mice, limiting our understanding of the mechanisms of stem cell communication during homeostasis. To interpret high dimensional signals that have complex spatial and temporal patterns, we combined optimized imaging of Ca2+ signaling in thousands of epidermal stem cells in living mice with a new machine learning tool, Geometric Scattering Trajectory Homology (GSTH). Using a combination of signal processing, data geometry, and topology, GSTH captures patterns of signaling at multiple scales, either between direct or distant stem cell neighbors. Here we show that epidermal stem cells display dynamic intercellular Ca2+ signaling among neighborhoods of up to 10 cells that is surprisingly coordinated and directed through time across a pool of thousands of stem cells. We find that this collective coordination is an emergent property of the stem cell compartment, distinct from excitatory quiescent neuronal tissues. We demonstrate that cycling stem cells, specifically G2 cells, govern homeostatic patterns of Ca2+ signaling. Stem cells in different cell cycle stages dynamically regulate localization of the gap junction component Connexin43 (Cx43). Lastly, we uncouple global from local communication and identify Cx43 as the molecular mediator necessary for connectivity between local signaling neighborhoods. This work provides resolution in how stem cells at different stages of the cell cycle communicate and how that diversity of phases is essential for tissue wide communication and signaling flow during epidermal regeneration. Our approach provides a framework to investigate stem cell populations and their signaling dynamics, previously not possible.

cell biology↗

bioRxiv2021

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

1One Sentence SummaryA novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimers disease and multiple sclerosis. 2 AbstractNeurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimers disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called diffusion condensation. By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

neuroscience↗