OpenFovea: open-source AFM data processing software

Roduit, Charles; Saha, Bhaskar; Alonso-Sarduy, Livan; Volterra, Andrea; Dietler, Giovanni; Kasas, Sandor

doi:10.1038/nmeth.2112

Cited by 49 publications

(43 citation statements)

References 6 publications

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“…To obtain quantitative values for cell surface changes caused by ethanol stress, we employed AFM in force spectroscopy mode, in which the cantilever and the tip successively approach and are retracted from the surface, and collected a large number of force curves (n ϭ 65,536) across a defined (usually away from any bud scar) region of the cell. Using appropriate software (OpenFovea [49]), these force curves were converted into forces versus indentation to generate elasticity maps ( Fig. 1a= and c=).…”

Section: Resultsmentioning

confidence: 99%

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Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

Schiavone

Formosa-Dague

Elsztein

et al. 2016

Appl Environ Microbiol

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A wealth of biochemical and molecular data have been reported regarding ethanol toxicity in the yeast Saccharomyces cerevisiae. However, direct physical data on the effects of ethanol stress on yeast cells are almost nonexistent. This lack of information can now be addressed by using atomic force microscopy (AFM) technology. In this report, we show that the stiffness of glucosegrown yeast cells challenged with 9% (vol/vol) ethanol for 5 h was dramatically reduced, as shown by a 5-fold drop of Young's modulus. Quite unexpectedly, a mutant deficient in the Msn2/Msn4 transcription factor, which is known to mediate the ethanol stress response, exhibited a low level of stiffness similar to that of ethanol-treated wild-type cells. Reciprocally, the stiffness of yeast cells overexpressing MSN2 was about 35% higher than that of the wild type but was nevertheless reduced 3-to 4-fold upon exposure to ethanol. Based on these and other data presented herein, we postulated that the effect of ethanol on cell stiffness may not be mediated through Msn2/Msn4, even though this transcription factor appears to be a determinant in the nanomechanical properties of the cell wall. On the other hand, we found that as with ethanol, the treatment of yeast with the antifungal amphotericin B caused a significant reduction of cell wall stiffness. Since both this drug and ethanol are known to alter, albeit by different means, the fluidity and structure of the plasma membrane, these data led to the proposition that the cell membrane contributes to the biophysical properties of yeast cells. IMPORTANCEEthanol is the main product of yeast fermentation but is also a toxic compound for this process. Understanding the mechanism of this toxicity is of great importance for industrial applications. While most research has focused on genomic studies of ethanol tolerance, we investigated the effects of ethanol at the biophysical level and found that ethanol causes a strong reduction of the cell wall rigidity (or stiffness). We ascribed this effect to the action of ethanol perturbing the cell membrane integrity and hence proposed that the cell membrane contributes to the cell wall nanomechanical properties. T he yeast Saccharomyces cerevisiae is a remarkable ethanol producer that is also very sensitive to its main fermentative product. At low to moderate concentrations (5 to 7%), ethanol mainly affects the growth rate, and at higher concentrations (Ͼ10%), it can strongly impair cell integrity, eventually leading to cell death with features of apoptosis (1). These inhibitory and toxic effects are ascribed to the fact that ethanol alters cell membrane fluidity and dissipates the transmembrane electrochemical potential, thereby creating permeability to ionic species and causing leakage of metabolites (2). Recent works using lipidomic methodologies confirmed the relationship between the composition of lipids, notably ergosterol and unsaturated fatty acids, and ethanol tolerance (3, 4). Moreover, as it diffuses freely into cells, ethanol at high concen...

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Section: Resultsmentioning

confidence: 99%

“…For both imaging and force spectroscopy, we used AFM Nanowizard III (JPK Instruments, Berlin, Germany). Data processing was realized using JPK data processing software as well as OpenFovea software (49). Isolation and determination of the biochemical composition of yeast cell walls.…”

Section: Methodsmentioning

confidence: 99%

Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

Schiavone

Formosa-Dague

Elsztein

et al. 2016

Appl Environ Microbiol

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“…The images of the cells recorded in quantitative-imaging mode (27, 28) allow analysis of all the force curves recorded in a single image (n ϭ 65,536). By applying a mask, thanks to the analysis software (OpenFovea [34,35]), only the force curves corresponding to the cells are extracted, leading to the elasticity maps presented in Fig. 3a, b, and c. In all the elasticity maps presented in this study, each pixel corresponds to a force curve that has been converted into an indentation curve and fitted with a Hertz model, from which a Young modulus (YM) value was extracted.…”

Section: Resultsmentioning

confidence: 99%

Nanoscale Effects of Caspofungin against Two Yeast Species, Saccharomyces cerevisiae and Candida albicans

Formosa

Schiavone

Martin-Yken

et al. 2013

Antimicrob Agents Chemother

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Saccharomyces cerevisiae and Candida albicans are model yeasts for biotechnology and human health, respectively. We used atomic force microscopy (AFM) to explore the effects of caspofungin, an antifungal drug used in hospitals, on these two species. Our nanoscale investigation revealed similar, but also different, behaviors of the two yeasts in response to treatment with the drug. While administration of caspofungin induced deep cell wall remodeling in both yeast species, as evidenced by a dramatic increase in chitin and decrease in ␤-glucan content, changes in cell wall composition were more pronounced with C. albicans cells. Notably, the increase of chitin was proportional to the increase in the caspofungin dose. In addition, the Young modulus of the cell was three times lower for C. albicans cells than for S. cerevisiae cells and increased proportionally with the increase of chitin, suggesting differences in the molecular organization of the cell wall between the two yeast species. Also, at a low dose of caspofungin (i.e., 0.5؋ MIC), the cell surface of C. albicans exhibited a morphology that was reminiscent of cells expressing adhesion proteins. Interestingly, this morphology was lost at high doses of the drug (i.e., 4؋ MIC). However, the treatment of S. cerevisiae cells with high doses of caspofungin resulted in impairment of cytokinesis. Altogether, the use of AFM for investigating the effects of antifungal drugs is relevant in nanomedicine, as it should help in understanding their mechanisms of action on fungal cells, as well as unraveling unexpected effects on cell division and fungal adhesion.T he yeast cell wall is composed of 50 to 60% ␤-glucans (glucose residues attached by 1,3-␤-and 1,6-␤-linkages), 40 to 50% mannoproteins (highly glycosylated polypeptides), and 1 to 3% chitin (1, 2). It is an essential dynamic structure playing roles in maintaining cell shape and integrity, sensing the surrounding environment, and interacting with surfaces and other cells (3). The cell wall represents 15 to 25% of the cell dry mass, the chemical composition of which is well established. Saccharomyces cerevisiae, also called baker's yeast, is the best-characterized eukaryotic model for scientific and biomedical research. Although the chemical composition of the yeast cell wall is well known, its molecular ultrastructure (organization or assembly) has not been extensively studied at nanoscale (4, 5), although there are a few reports on the nanomechanical and adhesive properties of the yeast cell wall under native conditions or under stress conditions (6-8). As for Candida albicans, it is by far the most common human-pathogenic fungal species. It can cause a range of pathogenic effects, including painful superficial infections, severe surface infections, and lifethreatening bloodstream infections (9). It is a major cause of morbidity and mortality in immunocompromised patients as a result of AIDS, cancer chemotherapy, or organ transplantation (10).Given its medical relevance, C. albicans has been the subject of extens...

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“…1B). Several researchers (Benitez et al, 2013; Crick and Yin, 2007; Dimitriadis et al, 2002; Gergely et al, 2000; Jaasma et al, 2006; Lin et al, 2007a, b; Melzak et al, 2010; Monclus et al, 2010; Nyland and Maughan, 2000; Polyakov et al, 2011; Radmacher, 2002; Roduit et al, 2012) have utilized analytical techniques aimed to automate CP selection and AFM force curve analysis for a variety of types of samples. While each method has its own strengths and weaknesses, AFM data is still plagued by low SNR at the contact point, making analysis difficult.…”

Section: Introductionmentioning

confidence: 99%

Automated AFM force curve analysis for determining elastic modulus of biomaterials and biological samples

Chang¹,

Raghunathan²,

Garland³

et al. 2014

Journal of the Mechanical Behavior of Biomedical Materials

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The analysis of atomic force microscopy (AFM) force data requires the selection of a contact point (CP) and is often time consuming and subjective due to influence from intermolecular forces and low signal-to-noise ratios (SNR). In this report, we present an automated algorithm for the selection of CPs in AFM force data and the evaluation of elastic moduli. We propose that the CP may be algorithmically easier to detect by identifying a linear elastic indentation region of data (high SNR) rather than the contact point itself (low SNR). Utilizing Hertzian mechanics, the data are fitted for the CP. We first detail the algorithm and then evaluate it on sample polymeric and biological materials. As a demonstration of automation, 64 x 64 force maps were analyzed to yield spatially varying topographical and mechanical information of cells. Finally, we compared manually selected CPs to automatically identified CPs and demonstrated that our automated approach is both accurate (< 10 nm difference between manual and automatic) and precise for non-interacting polymeric materials. Our data show the algorithm is useful for analysis of both biomaterials and biological samples.

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OpenFovea: open-source AFM data processing software

Cited by 49 publications

References 6 publications

Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

Evidence for a Role for the Plasma Membrane in the Nanomechanical Properties of the Cell Wall as Revealed by an Atomic Force Microscopy Study of the Response of Saccharomyces cerevisiae to Ethanol Stress

Nanoscale Effects of Caspofungin against Two Yeast Species, Saccharomyces cerevisiae and Candida albicans

Automated AFM force curve analysis for determining elastic modulus of biomaterials and biological samples

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