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mental samples are membrane vesicles. ... position of the samples using the two ... scat and average jump (instead of D. BM. ) 5. Simulations. We test our two ...
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Analysis of nanoparticles mixtures from the environment 3

3

Nina Paffoni , Marc Bailly-Bechet , Yasmina Fedala , Justine Voronkoff , 3 Claude Boccara , Chris Bowler1 and Martine Boccara1,2 1

2

1.Nanoparticles in biology Viruses are the most abundant entities on Earth. In addition of being human pathogens they play important roles in regulating bacterial communities and in biogeochemical cycles [1]. Other very common entities in environmental samples are membrane vesicles. There are more and more reports of the ’vesiculome’ of various biotopes. Membrane vesicles are secreted by all organisms, they are made of lipids and proteins and may contain nucleic acids. Their role is only starting to be revealed. It is crucial to quantify and distinguish these biotic nanoparticles (viruses and membrane vesicles) in any environment to estimate their respective abundance, in order to understand the environment ecology.

3. Key challenge 3. Key challenge

We want to distinguish different types of We want to distinguish different types of particles both by size and refractive index particles both by size and refractive index and estimate their proportion. and estimate their proportion.

We developed an optical microscope which use interferometry to detect nanoparticles. To characterize further the particles we determine their diameters with two different methodologies : - Dscat: from the maximum intensity of their scattering signal, with a refractive index n=1.5 corresponding to viruses density. - DBM : from the average jump between two successive frames, which is a signal inversely related to the volume of the particle, average jump and max intensity

DBM Dscat

acquisition

tracking

5. Simulations

We propose two methods based on density estimation. Method 1 : a) We cluster data according only to Dscat.

We test our two methods on simulated samples (N=1000) in order to compare them.

iru se s nd ex of v ei

metrical, we fit each group found in a) with a modified normal law with positive skew.

small viruses

determination of diameters

4. Methods

b) Because the distribution of DBM is asym-

tiv

trajectory analysis

Method1 method2

re fra c

Scat diameter (nm)

larger viruses

2. Data : particles diameter determined by interferometry and by Brownian Motion

vesicles

Cases Typical Ratio of with error on correct good the predic#cluster means tions

2 viral pop. (30nm & 50nm)

98% 99%

0.53 0.47

0.89 0.91

2 viral pop. (30nm & 50nm) + vesicles

51% 94%

3.57 1.94

0.25 0.71

10% 98%

27.35 16.54

0.40 0.53

BM diameter (nm)

The purpose is to establish a clustering The purpose is to establish a clustering analysis method to analyze the comanalysis method to analyze the composition of the samples using the two position of the samples using the two measurements of diameter. measurements of diameter.

BM diameter (nm)

Method 2 : We use a classic Gaussian Mixture Model with the R’s package Mclust[2], the two dimensions being Dscat and average jump (instead of DBM)

3 viral pop. (30nm, 50nm & 80 nm) + vesicles

The second method is clearly better.

Conclusion & Perspectives

We analyzed measurements of particle diameters from various purified viruses. We also applied our method to analyze marine samples from TARA Oceans. Because viruses constitute 90 % of the biomass in the seas, distributions of marine viruses and vesicles are indicatives of the richness of the local environment[3].

We use our analysis method on samples from 18 different TARA stations to compare them. And we plan to challenge these results with metagenomic analysis. These are our first results :

Scat diameter (nm)

This is an example on purified sample of λ phage (the capside is about 60 nm)

Scat diameter (nm)

6. Biological samples analysis

viruses

vesicles BM diameter (nm) Lambda phage (N=747) (N=747

Here we identify vesicles (dark blue cluster) which have lower refractive index and three types of virus with different sizes.

BM diameter (nm)

Viruses

TARA station 122 (N=4029)

Contact [email protected] [email protected] 1. Institut de Biologie de l'Ecole Normale Supérieure 2. Atelier de Bioinformatique/MNHN 3. Institut Langevin, ondes et images/ESPCI

Vesicles

References 1. J. A. Fuhrman. Marine viruses and their biogeochemical and ecological effects. Nature, 399(6736):541–548, Jun 1999. 2. Chris Fraley and Adrian E. Raftery. Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association, 97:611–631, 2002. 3. S. J. Biller, F. Schubotz, S. E. Roggensack, A. W. Thompson, R. E. Summons, and S. W. Chisholm. Bacterial vesicles in marine ecosystems. Science, 43(6167):183–186, Jan 2014.