Biology subjects

Johansson, M. M.

Publications and source records attributed to Johansson, M. M..

3 recordsLinked to original sources

bioRxiv2025

Label-Free Live-Cell Imaging improves Mode of Action Classification

Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free live-cell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single-time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modeling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

bioinformatics↗

bioRxiv2025

Single-cell morphological profiling reveals insights into cell death

Analysis at the single-cell level is a powerful approach to study biological processes and responses to perturbations. However, its application in morphological profiling with phenomics remains underexplored. Here, we use the Cell Painting assay to investigate morphological effects of 53 small molecule compounds, associated with six distinct programmed cell death mechanisms, across six concentrations in MCF7 cells. To compare single-cell and aggregated analysis strategies, we conduct both supervised and unsupervised evaluations aimed at identifying features linked to programmed cell death. We apply an energy distance as a metric to quantify morphological perturbation strength, enabling efficient filtering. Among three tested feature extraction methods, self-supervised DINO embeddings applied to single-cell data captured high-resolution morphological patterns. Focused analyses of apoptosis-inducing compounds revealed biological heterogeneity attributable to specific molecular targets and concentration-dependent effects, which were not apparent in aggregated profiles. In contrast, multi-class classification models for the six programmed cell death mechanisms trained on single-cell features achieved F1 scores of 79.86%, while models trained on aggregated features reached F1 scores of up to 89.97%. Our results highlight the advantages of single-cell data for unsupervised exploration and show that aggregated representations yield more robust and accurate performance in supervised models.

bioinformatics↗

bioRxiv2020

The binding mechanism of Streptococcus suis accessory virulence factor and adhesin SadP to globotetraosylceramide

Streptococcus suis is part of the pig commensal microbiome and a major pathogen causing pneumonia and meningitis in pigs and occasionally also zoonotic meningitis. According to genomic analysis, S. suis is divided into asymptomatic carriage, respiratory and systemic strains with distinct genomic signatures. The virulence factor S. suis adhesin P (SadP) recognizes the galabiose Gal1-4Gal-oligosaccharide. Based on its oligosaccharide fine specificity, SadP can be divided into subtypes PN and PO. We show here that subtype PN is distributed in the systemic strains that cause meningitis, whereas type PO is found in asymptomatic carriage and respiratory strains. Both types of SadP are shown to predominantly bind to pig lung globotriaosylceramide (Gb3). However, SadP adhesin from systemic subtype PN strain also binds to globotetraosylceramide (Gb4). Mutagenesis studies of the galabiose-binding domain of type PN SadP adhesin showed that the amino acid asparagine-285, which is replaced by an aspartate residue in type Po SadP, was required for binding to Gb4 and, strikingly, it was also required for interaction with the glycomimetic inhibitor phenylurea-galabiose. Molecular dynamics simulations provided further insight into the role of Asn-285 for Gb4 and phenylurea-galabiose binding, suggesting additional hydrogen bonding to terminal GalNAc of Gb4 and urea-group. Thus, the Asn-285-mediated molecular mechanism of type PN SadP binding to Gb4 could be used as a candidate to selectively target S. suis in invasive systemic disease without interfering with commensal strains, which may open up new venues for developing intervention strategies against this pathogen.

microbiology↗