Dataset Information

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

ABSTRACT:

Motivation

In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.

Results

We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis.

Availability and implementation

https://github.com/trevor-chan/Melanoma_NetworkMorphology.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chan TJ

PROVIDER: S-EPMC9825265 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

Chan Trevor J TJ Zhang Xingjian X Mak Michael M

Bioinformatics (Oxford, England) 20230101 1

<h4>Motivation</h4>In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.<h4>Results</h4>We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YU ...[more]

PMID: 36610710

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Project description:MotivationCancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution.ResultsHere, we apply a bioinformatics approach to a single cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity.AvailabilityThe data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Background: Staphylococcus epidermidis is a common skin commensal that has emerged as a pathogen in hospitals, mainly related to medical devices-associated infections. Noteworthy, infection rates by S. epidermidis have the tendency to rise steeply in next decades together with medical devices use and immunocompromized population growth. Staphylococcus epidermidis population structure includes two major clonal lineages (A/C and B) that present contrasting pathogenic potentials. To address this distinction and explore the basis of increased pathogenicity of A/C lineage, we performed a detailed comparative analysis using phylogenetic and integrated pangenome-wide-association study (panGWAS) approaches and compared the lineages's phenotypes in in vitro conditions mimicking carriage and infection. Results: Each S. epidermidis lineage had distinct phenotypic signatures in skin and infection conditions and differed in genomic content. Combination of phenotypic and genotypic data revealed that both lineages were well adapted to skin environmental cues. However, they appear to occupy different skin niches, perform distinct biological functions in the skin and use different mechanisms to complete the same function: lineage B strains showed evidence of specialization to survival in microaerobic and lipid rich environment, characteristic of hair follicle and sebaceous glands; lineage A/C strains showed evidence for adaption to diverse osmotic and pH conditions, potentially allowing them to occupy a broader and more superficial skin niche. In infection conditions, A/C strains had an advantage, having the potential to bind blood-associated host matrix proteins, form biofilms at blood pH, resist antibiotics and macrophage acidity and to produce proteases. These features were observed to be rare in the lineage B strains. PanGWAS analysis produced a catalog of putative S. epidermidis virulence factors and identified an epidemiological molecular marker for the more pathogenic lineage. Conclusion: The prevalence of A/C lineage in infection is probably related to a higher metabolic and genomic versatility that allows rapid adaptation during transition from a commensal to a pathogenic lifestyle. The putative virulence and phenotypic factors associated to A/C lineage constitute a reliable framework for future studies on S. epidermidis pathogenesis and the finding of an epidemiological marker for the more pathogenic lineage is an asset for the management of S. epidermidis infections.

Dataset Information

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

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