WO2011154650A2 - Method for identifying mammalian cells by maldi-tof mass spectrometry on whole cells - Google Patents

Abstract

The present invention relates to a method for identifying a given type of mammalian cells in a biological sample, to be analyzed, which may contain mammalian cells, characterized in that the following successive steps are carried out, in which steps: 1/ at least one reference spectrum of at least one reference sample of at least one given type of mammalian cells is established by "MALDI-TOF" ("matrix-assisted-laser-desorption-ionization- time-of flight") mass spectrometry, and 2/ if necessary, the whole cells contained in said biological sample to be analyzed are extracted so as to obtain an extract of eukaryotic cells, and 3/ a spectrum of a said extract of whole cells obtained in step 2/ is produced by MALDI-TOF mass spectrometry, and 4/ the spectrum obtained in step 3/ is compared with a plurality of reference spectra produced in step 1/ from a plurality of reference samples of a plurality of various given types of mammalian cells.

Description

 Method for identifying mammalian cells by whole cell MALDI-TOF mass spectrometry

The present invention relates to a method of identifying mammalian cells in a biological sample. Mammalian cells are here understood to mean both cells taken directly as such, according to the invention, said mammal or cultures of cells derived from mammalian cells, especially differentiated cells or immortalized cell lines, as explained hereinafter. . The living consists of three worlds, the world of eukaryotic cells (cells with a nucleus), the world of bacteria and the world of archae (cells lacking a nucleus). The eukaryotic world includes unicellular (protozoan) and multicellular (metazoan) beings. Numerous biological parameters differentiate the three worlds of the living genomically as well as on the protein and functional levels.

Eukaryotic cells do not have a wall while the plasma membrane of bacteria is surrounded by a lipopolysaccharide-rich (LPS) osidic "wall" for Gram-negative bacteria, peptidoglycan (PGN) for Gram-positive bacteria, or lipoarabinomannan for mycobacteria. The plasma membrane of eukaryotic cells (which makes it possible to establish a boundary between internal and external medium) consists of a lipid bilayer in which many glycosylated or non-glycosylated proteins are embedded. These are all or part of these proteins that allow homotypic adhesion (from a cell of a type to a cell of the same type) or heterotypic eukaryotic cells (from a cell of a type to a cell of a cell). other type), and thus tissue cohesion. A notable exception to this general principle of tissue organization is the blood which is a liquid in which there are individual cells that do not adhere to each other, say circulating cells.

The properties of intracellular proteins also differ depending on whether they are bacterial or eukaryotic proteins. By way of example, the tyrosine phosphorylation widely represented in the eukaryotic world is absent (or weakly present) in the bacterial world.

Finally, a large number of metabolic pathways are specific to the eukaryotic world (transducing cascades, intracellular messengers, immune response molecules such as cytokines, etc.).

In other words, the genetic, molecular and functional structure of eukaryotic cells is completely different from that of bacteria, and the structure of eukaryotic cells can not be inferred from that of bacteria.

However, the eukaryotic world is extremely diverse since it comprises unicellular organisms and multicellular organisms, organisms whose dimensions are of the order of a few micrometers and organisms whose dimensions are of the order of a hundred meters , organisms whose weight is of the order of one nanogram and others of thousands of tons, etc. Therefore, only mammalian cells, especially the cells, are taken into account in the present invention. circulating cells) and cells of the immune response with, as control cells, differentiated cells from said circulating cells and immortalized cell lines from other tissue types, amoebae and a xenopus cell line.

The blood cells consist on the one hand of red blood cells (red blood cells), responsible for the oxygenation of the body and, on the other hand, of different types of leucocytes (white blood cells) involved in the natural and / or specific immune response. There are three major types of circulating leukocytes: lymphocytes, monocytes and neutrophils.

Over the past 40 years, it has emerged that lymphocytes consist of two major subpopulations, T lymphocytes and B lymphocytes. More recently, T lymphocytes have been subdivided into T lymphocytes and Τ-lymphocytes. δ, and that one can distinguish in the population of T lymphocytes lymphocytes which express CD4 or CD8 and that they themselves can group several populations. In addition, each type of immune cell is sensitive to activation by exogenous agents (microorganisms, chemical agents) or endogenous agents (mediators of the immune system, hormones, etc.). These different types of activation are difficult to evaluate globally and do not always correspond to stable states. Thus, cytokines such as interleukin (IL) -4 on one side and interferon (IFN --- on the other are known to polarize Ta- lymphocytes (Th1 in response to IFN- or Th2). in response to IL-4).

Moreover, circulating monocytes are the precursors of tissue macrophages whose microbicidal and tumoricidal functions are controlled by the microenvironment of these macrophages. Macrophages result from the differentiation of circulating monocytes when they reach tissues. They are at the heart of the immune response: they are capable of destroying infectious agents or tumor cells; they influence the response of lymphocytes which, in turn, control their activity. Thus, by analogy with the Th1-Th2 dichotomy, it has been proposed to call M1 macrophages fully microbicides and tumoricides (macrophages responding to a cytokine such as IFN-gamma) and macrophages M2 macrophages that respond alternatively (response to IL-4, IL-10 or Transforming Growth Factor (TGF) -). The functional properties of these macrophages are quite diverse since they can be little or no microbicides and that they can modulate the immune response more or less effectively.

Current methods for identifying mammalian cells, such as circulating cells, mainly comprise the methods a / c discussed below. a / - Methods based on the dyeing and cytochemical properties of circulating cells.

These methods are based on the morphology and the dye or cytochemical properties of the cells and make it easy to identify lymphocytes, monocytes and neutrophils.

On the other hand, the dyeing, cytochemical and physical properties of the circulating cells do not make it possible to recognize and isolate the lymphocyte and monocyte subpopulations and, a fortiori, the sub-populations poorly represented. They do not therefore allow to appreciate the functional states of these cells that define functional subpopulations, or even abnormal populations with subtle changes likely to appear during pathological processes. b / - Biological methods for characterization of circulating cells by flow cytometry

The breakthrough in identifying lymphocyte subpopulations has been the use of monoclonal antibodies. When the cell population to be analyzed can be characterized by a specific marker and such antibodies are available (for example, anti-CD4 antibody, anti-CD8 antibody), the study of CD4 + T cell populations and CD8 + T lymphocytes are performed in both research and hospital settings. Flow cytometry is a technique that analyzes in a few minutes the fluorescence of individual cells within large cell samples. The constraint of flow cytometry is that it is necessary to have specific antibodies which are rendered fluorescent after direct or indirect labeling (after binding to the antibody molecule of a specific antibody of species or of isotype, itself fluorescent). It is thus possible to detect within a given cell population the presence of a particular cell type. For example, the presence of T cells in mononuclear cells (anti-CD3 antibodies) or CD4 + T cells (anti-CD3 antibodies labeled with a fluorochrome and anti-CD4 antibodies labeled with another fluorochrome can be tested for ).

Flow cytometry is a technique that is not only analytical but also preparatory. Dedicated cytometers make it possible to separate, after their analysis, the cells labeled with specific antibodies from unlabeled (or differently labeled) cells. It is thus possible to harvest the cells possessing such or such antigenic property.

Flow cytometry, however, has certain limitations as an analytical technique.

There is usually no isolated marker that is specific to a given cell population. Only a combinatorics of markers makes it possible to define the various lymphocyte or macrophage subpopulations. In this context, multi-color cytometers are essential since each marker must be associated with a given fluorochrome. The flow cytometry approach experiences an additional degree of complexity when the markers are intracellular molecules, which requires prior cell permeabilization. When these markers are inducible molecules that can be secreted (often cytokines), it is necessary to include in the experimental scheme export inhibitors in order to increase the concentration. intracellularly said marker and its detection. These approaches, which are more difficult to implement, are usually reserved for research protocols. Thus, the demonstration of regulatory T cells requires the combined use of membrane (CD4 and CD25) and intracellular (Foxp3) markers.

The situation is even more complex with myeloid cells: monocytes and macrophages can be roughly differentiated from each other by the respective expression of CD14 and CD68. But, there are no phenotypic markers that can characterize their responses to the stimuli they are subjected to. At most the CD163 molecule would allow to characterize the macrophages activated by IL-4. It thus appears that flow cytometry does not make it possible to characterize the functional state of the myeloid cells (circulating monocytes and / or macrophages). Limitations of flow cytometry as a preparatory technique

Despite an old marketing, this technique remains expensive, heavy to implement and requires specialized personnel. It remains restricted to a small number of specialized laboratories. c / - Microarray method Very recently, high-throughput gene expression techniques such as microarray microarrays have made it possible to characterize different functional populations of macrophages from many parameters.

This method has certain limitations because it requires the availability of a given cell type (for example, isolated macrophages by a conventional method) which is then compared with other cell types (such as T cells) or ex vivo is activated in order to obtain so-called activated macrophages (see below). It is also a heavy technique requiring the production of RNA in large quantities and high quality. The analysis of the data is delicate and requires time incompatible (often, many weeks) with clinical research useful to the patient. Finally, gene expression is very versatile and does not have the robustness of protein expression used in flow cytometry to identify cell populations.

The object of the present invention is to provide a new method for identifying a given type of mammalian cells in a biological sample to be analyzed, which may contain mammalian cells.

More particularly, another object of the present invention is to provide a novel method for identifying mammalian cells, which makes it possible to characterize a cell type that is not or can be characterized by specific antibodies.

Mass spectrometry is a physical analysis technique for detecting and identifying molecules of interest by measuring their mass. Its principle lies in the gas phase separation of charged molecules (ions) according to their mass / charge ratio (m / z). It also makes it possible to characterize the chemical structure of the molecules by fragmenting them. Its easy, fast and inexpensive use has spread widely in many scientific fields, including biology. Thus, combined with other approaches, it allows the study of the proteome. Recently extended to medicine, it makes it possible to study the serum of patients, a complex mixture of proteins, which can be useful for the diagnosis of various diseases. Mass spectrometry under its MALDI-TOF variant (matrix-assisted laser desorption ionization-time-of-flight) has recently entered the microbiology laboratories. It allows the detection of bacterial toxins. It also identifies and classifies different bacterial types using intact bacteria. Indeed, among the various components identified in a spectrum of MALDI-TOF mass spectrometry, some seem specific to a specific bacterial species. Thus, the comparison of their spectra allowed in 2007 to identify different species and subspecies of staphylococci coagulase-. The spectra also differ according to whether Staphylococcus aureus strains are resistant to certain antibiotics or not. Thus, it was possible to identify in 2008 a large number of bacterial species in clinical samples using databases made from each of these species. The articles of ZHANG et al. (Journal of American Society for Mass

Spectrometry. Vol 17. April 1, 2006. Pages 490-499) and KARGER et al. (Journal of Virological Methods, vol 164. Mar. 1, 2010. Pages 116-121) relate to methods of identifying protein extracts of eukaryotic cells and not whole cells. Because the cells are lysed, intracellular proteins are released, including cytosolic proteins that ionize and are more easily detected because of their small size and solubility, in contrast to cell membrane surface proteins, which exhibit high molecular weights and are less soluble than cytosolic proteins, making them more difficult to detect by mass spectrometry.

In addition, in the article by ZHANG et al., In FIG. 5, the authors compared the spectra obtained from a category of cells (BHK21 cells = hamster fibroblasts), namely a spectrum obtained from the unlysed cells (a) and spectra obtained from the same lysed cells (b). The authors observe that the spectra obtained from the lysed cells comprise many more peaks.

As a result, the article by ZHANG et al. provides no suggestion or incentive to use the whole cell assay method according to the present invention to specifically identify a cell type. On the contrary, the article by ZHANG et al. is a prejudice against the method according to the invention, insofar as the authors recommend mass spectral analysis of protein extracts obtained on lysed cells. In the article by KARGER et al., The cells are incubated with various solvents (see page 118, 1st paragraph, right column: ethanol, then formic acid, then acetonitrile), said solvents making it possible to extract the proteins. The study therefore focuses on total proteins, extracted from cells lysed with different solvents and not on whole cells.

In the article by ZHANG et al., Lysis of the cells is carried out with the matrix liquid used for the spectrometric analysis, namely the compound DHB (2,5-dihydroxybenzoic acid) and the protein extracts are rinsed several times. with DHB, prior to performing the spectrometric analysis.

On the other hand, in the article by ZHANG et al., The authors merely seek to identify specific protein peaks for each cell type.

Finally, MALDI-TOF mass spectrometry has made it possible to characterize various spores of fungi, yeasts and pollens, in particular in US 2003/027231.

It has been found, according to the present invention, that it is possible to identify, i.e. to characterize in a specific manner, from whole cells (i.e. cells not previously lysed before analysis) of mammalian eukaryotic cell types by MALDI-TOF mass spectrometry, although only cytosolic proteins (intracellular type) ionize and are detected due to their small size and solubility and not, as in the flow cytometry method, the surface proteins of the cell membrane, which have much higher molecular weights ranging from 30 to 100 kDa and require the use of antibodies to reveal their presence, and although the intracellular proteins detectable by mass spectrometry are relatively small in number.

But this was made possible by taking into account all the peaks of the spectra, that is to say by a combinatorial analysis of the spectra involving spectrum-to-spectrum comparison and not the search for specific peaks in them. - taken separately.

The inventors formulate the hypothesis that this specific characterization results from the fact that, during mass spectrometry of the MALDI-TOF type, said intracellular proteins are in sufficient number and their nature and their relative proportion are sufficiently distinct and variable and therefore specific. between the different eukaryotic cell types to allow a specific characterization of the cell type by a mass spectrum analysis of this combination of proteins, provided that it is not sought to identify said cells from specific peaks, corresponding to specific proteins, but by a combinatorial analysis of peaks, namely by comparison of complete spectra, in particular of at least 70 peaks. This characteristic of the method according to the invention is important because it is this combinatorial analysis of the set of peaks of the spectra which allows, according to the present invention, to specifically identify cells mammals, without having to know which ones of the peaks are actually specific and therefore unique for the category of cells concerned, as suggested in the article by ZHANG et al. And, it is also this combinatorial analysis which makes it possible to obtain the specific identification of mammalian cells from the analysis of whole cells and not of protein extracts.

Indeed, according to the present invention, the whole cells are not subjected to any lysis treatment, prior to their mixing with the matrix solution. Indeed, the cells according to the present invention are taken up in washing buffer, in particular PBS, so that they remain intact (unlysed) before being mixed with the matrix solution for analysis by mass spectrometry.

When the cells are incubated beforehand with the matrix, as in the article by ZHANG et al., The cell extracts are diluted, which of course causes loss of proteins and therefore of information, because the methods of Prior lysis "selects" certain proteins, which may be different, in addition, depending on the lysis method. The method according to the present invention is therefore simpler and faster to perform, having no protein extraction step prior to the analysis to be performed.

The inventors have thus demonstrated specific signatures of different types of mammalian cells by analysis of the complete spectra obtained by MALDI-TOF mass spectrometry.

According to the present invention, the demonstration of specificity was performed on comparative analyzes of 22 different cell types while the article of ZHANG et al. is satisfied with the analysis of only 3 types of mammalian cells, which are, in addition, only immortalized cell lines.

More specifically, the present invention provides a method for identifying a given type of eukaryotic cells in a biological sample, to be analyzed, capable of containing mammalian cells, characterized in that the following steps are carried out in which:

1 / at least one reference spectrum is established by mass spectrometry, called MALDI-TOF ("matrix-assisted-laser-desorption-ionization-time-of-flight"), of at least one reference sample, of at least one given type of mammalian cells, said sample of reference consisting of purified whole mammalian cells containing at least 95% of cells of said given type, and

2 / extraction of the whole cells contained in said biological sample to be analyzed is carried out, if necessary, to obtain an extract of whole cells, and

3 / a MALDI-TOF mass spectrometric spectrum of a said whole cell extract obtained in step 2 / is produced, and

4 / the spectrum obtained in step 3 / is compared with:

4a / a reference spectrum of a purified extract of said given type of mammalian cells previously made in step 1 /, if one only seeks to identify whether said sample to be analyzed comprises a mammalian cell type of said given type mammalian cells, or

4b / a plurality of reference spectra made in step 1 / from a plurality of reference samples of a plurality of different given types of eukaryotic cells, said plurality of reference spectra comprising a spectral library of reference of different given types of mammalian cells, if one seeks to identify whether said sample to be analyzed comprises a mammalian cell type selected from different given types of mammalian cells whose spectra are included in said reference spectrum bank , and

5 / - a mammalian cell type of the extract obtained in step 2 /, from said sample to be analyzed, is identified in at least one of two ways:

5a / is identical to a said given type of mammalian cells, if, in step 4 / above, is found in the spectrum obtained in step 3 / all the characteristic peaks a said reference spectrum comprising at least 50 peaks, preferably at least 70 peaks,

5b / is as being close to at least one said given type of mammalian cells, by characterizing the proximity of the spectrum obtained in step 3 / with respect to at least one said reference spectrum of at least one said given type mammalian cells, by determining the number of characteristic peaks in common between the spectrum obtained and that of at least one said reference spectrum of at least one said type of mammalian cells. Preferably, in known manner, the proximity between an analyzed spectrum and a plurality of reference spectra can be determined by a hierarchical classification analysis, also called "clustering" and graphically represented in the form of a dendogram according to the number of peaks in common. that they share with each other, as explained in the examples below.

The term "biological sample of whole cells" means any sample from a sample of blood tissue or other tissue, including a clinical sample of a patient, or a cell culture sample. By "mammalian cells" is meant here:

"mammalian" primary cells, that is to say cells taken directly from said mammal and which are not immortalized, and

cells derived from said primary mammalian cells, that is to say cells obtained by culturing said primary mammalian cells, including:

differentiated cells, and

immortalized cell lines. Among mammalian cells, there are more particularly blood cells, also called "circulating cells", and cells of other tissues.

According to the present invention, one seeks more particularly to identify immune cells, also called "cells of the immune response", but also other types of cells, such as epithelial cells and fibroblast cells and trophoblast cells.

Among the circulating cells, it is more particularly intended to identify, according to the present invention, the cells of the immune response of the blood, namely circulating leukocytes comprising monocytes, T lymphocytes, polynuclear neutrophilic cells and cells other than cells of the response. immune system, such as red blood cells. Among the cells of the immune response of organs other than the blood, it is more particularly sought to identify, according to the present invention, cells of lymphoid organs, such as the liver, the spleen, the thymus and the bone marrow, in particular lymphocyte and macrophage cells, but also immune cells of non-lymphoid organs, such as the placenta, in particular placenta trophoblast cells.

Among the differentiated cells obtained by circulating cell cultures of cells, it is more particularly sought to identify (a) differentiated monocytes in the form of macrophages or dendritic cells and (b) differentiated lymphocytes in the form of lymphoblasts and other types of cells dendritic.

Likewise, according to the present invention, "cell type" is understood to mean a said mammalian cell or cell derived from mammalian cells of a particular nature given in a given normal or pathological state of the original tissue and / or in an activation state by a given activation factor of said cells where appropriate. More particularly, mammalian cells are human cells.

In particular, in the case of macrophages, cytokines are known activation factors for modulating the response of macrophages and inducing characteristic activation states, in particular the cytokines chosen from IL-4, IL-10 , IFN--, Tumor Necrosis Factor (TNF), TGF - 1. Macrophages are also known to be stimulated by activating factors such as lipopolysaccharides (LPS) or peptidoglycans (PGN). In step 1 /, the reference samples contain 100% of said cells, in the case of differentiated cells and cell lines, and at least 95% of said whole cells in the case of primary cells.

Here, the term "peak characteristic", a peak whose relative intensity, expressed as a percentage of the intensity of the peak of greater intensity, is greater than at least 3 times that of the background noise.

Here is meant by "peak in common between the spectrum obtained and a reference spectrum", a peak whose molecular mass does not differ by more than ± 1 Da from the reference peak molecular weight and having a relative intensity does not deviate by ± 20% from the relative intensity value of said peak in the reference spectrum expressed as a percentage of the intensity of the peak of greater intensity in the spectrum, as explained below.

MALDI-TOF mass spectrometry makes it possible to characterize whole eukaryotic cells in the absence of any prior labeling, which constitutes a major advance over existing techniques, in particular flow cytometry.

This approach is simple to implement, reproducible, fast and inexpensive. It is sufficiently resolutive to allow to characterize different activation states of a given cell population, especially the activation states of human macrophages.

A resulting spectral database identifies any unknown cell population as belonging or not to the cell populations present in the spectral base.

This database can therefore, in addition, be used to characterize different types of eukaryotic cells during their isolation from normal or pathological tissues containing several cell populations.

In a preferred embodiment, in step 2, the different types of whole cells contained in said whole cell extract are separated to obtain at least one purified extract containing at least 95% of a type of whole cells. . Physical methods of separating different cell types of circulating cells are well known to those skilled in the art. They are essentially based on the observation of the differences in density of the circulating cells. Thus, red blood cells and neutrophils have a density greater than 1.077 and monocytes and lymphocytes have a density of less than 1.077. When the circulating cells are deposited on a barrier of density equal to 1.077 and the cells are centrifuged, the cells are positioned according to their density: the red blood cells and the neutrophils are located under the liquid (Ficoll solution). density 1.077 and the mononuclear cells, monocytes and lymphocytes, lie on the surface of this Ficoll solution. It is therefore sufficient to collect red blood cells and polynuclear cells, on the one hand, and mononuclear cells, on the other hand.

Other "physical" properties made it possible to extract neutrophils from the mixture of red blood cells + polynuclear neutrophils. A first method of extraction is to perform a osmotic shock to which red blood cells are particularly sensitive since they are lysed after 30 s of contact with water. Neutrophil polynuclear cells are thus recovered, the properties of which can, however, be more or less altered. The second method of extraction is to incubate the red blood cell + neutrophil mixture with dextran T-500 at a final concentration of 1.5%, which causes the agglutination of red blood cells. These agglutinated red cells sediment much faster than neutrophils and, after about 30 min, can be recovered supernatant highly enriched neutrophils.

Finally, the adhesive properties of monocytes are used to separate monocytes and lymphocytes. Thus, incubated in medium containing 10% fetal calf serum, the monocytes adhere to the plastic culture dishes in 30 to 60 min, but not the lymphocytes. The lymphocytes can be recovered in the culture supernatant and the adherent cells are considered to be monocytes.

Other preparatory methods for circulating cells by positive or negative selection are known, such as an increasingly popular method of isolating an increasingly common cell type, by coupling magnetic-specific antibodies to magnetic beads. and placing these magnetic beads in a column backed by a magnet. When the cell mixture is deposited on the column, only the cells recognized by the antibody present on the surface of the magnetic beads are retained, the others are collected ("negative" selection of cells that do not express the selected phenotypic character). The magnet can then be moved away from the column, which has the effect of unhooking the cells of this column and can thus be recovered ("positive" selection of the cells which express the selected phenotypic character). Monocytes can thus be purified using anti-CD14 antibodies or T cells with anti-CD3 antibodies. Methods for the preparation of cell types constituting a tissue other than blood tissue, such as, for example, the placenta, are also known.

The methods for isolating the different types of cells present in a tissue comprise the following steps:

a step of washing and cutting the fabric into small fragments,

an enzymatic digestion step for dissociating the cells from each other, one or more stages of separation of the various cell types by physical method making use of their physical properties (difference in density) or by "biological method" by positive selection or negative of a given population. Biological methods require the use of antibodies whose specificity is known (flow cytometry, affinity columns),

methods for monitoring these separation steps in order to ensure the quality of the purification of the different cell types.

Advantageously, a spectrum of a given type of cell is made from a cell extract having a cell concentration of at least 10 ⁵ cells / 1 μl of said cell type.

This threshold of 10 ⁵ cel lu les / μΙ must be respected to achieve the reference spectra in step 1 /, as well as the spectra of the sample to be analyzed in step 3 /.

More particularly, in steps 1 / and 3 /, said spectra of reference samples and spectra of samples to be analyzed are taken into account only if said spectra have a quality score greater than 2, said quality score consisting of the addition of the two notes ni and n2 each having a value of 0 to 6 with: ni is the score given according to the number of characteristic peaks of said spectrum n, as specified more specifically in example 2 below, and

n2 is the score given according to the value of the signal-to-noise ratio (Rmax) of the peak of greater intensity of said spectrum, as specified more precisely in example 2 below.

More particularly, the peaks of said spectra are defined by a pair of coordinates composed of an abscissa consisting of the mass / charge ratio (m / z) of 2000 to 15000 Da, more particularly 3000 to 7000 Da, the coordinate in ordinate being a relative intensity or in arbitrary units, and the peaks of greater intensities are preferably retained in the spectrum.

By "relative intensity" is meant here an intensity expressed as a percentage of the intensity of the peak of greater intensity or otherwise expressed by the ratio of (intensity of the peak / intensity of the background noise).

The more important numbers of peaks are integrated, the more reliable the database of reference spectra will be. The inventors have determined that below 50 peaks, there is no guarantee of being able to reliably differentiate all mammalian cell types. More particularly, it is sought to identify a given type of cell and said reference spectra are made for at least one given type of cell selected from the following 22 types of eukaryotic cells: a / mammalian cells: - human monocytes

- human T lymphocytes

- neutrophilic neutrophils

- human red blood cells - human macrophages differentiated from monocytes

- murine macrophages differentiated from bone marrow

- myelomonocytic human cell line THP-1

- J774 immature macrophage murine cell line - DH82 macrophage canine cell line

- differentiated dendritic cells of monocytes

- C8166 human cell line of expressing lymphocytes

CCR5

- fibroblastic murine cell line L929 - HeLA epithelial human cell line

epithelial type human cell line 293T

- human cell line of trophoblastic type JEG

BeWo trophoblastic type human cell line

placental cells of human trophoblasts b / non-mammalian eukaryotic cells:

- XTC-2 cell line of Xenopus laevis

- amoebae cells Acanthamoeba polyphaga

- Acanthamoeba amoebae cells caste / year //

- amoebae cells Hartmanella vermiformis - amoebae cells Poteriochromonas melhamensis.

The above-mentioned cell lines are immortalized cell lines, i.e. they have acquired the property of to multiply a large number of times over very many generations (some cell lines have been in existence for more than fifty years by being cultivated twice a week) whereas the so-called primary cells can only multiply a limited number of times: Beyond a certain limit of a few dozen times, these primary cells can no longer divide and die.

Even more particularly, the reference spectra of the 22 types of eukaryotic cells comprise the 70 peaks of greater intensity mentioned in the following Tables 1 to 22, in which the different characteristic peaks are characterized by their coordinates on the abscissa m (Da) / z and their ordinates of relative intensity expressed as a percentage of the intensity of the peak of greater intensity (%).

In Tables 1 to 29 below: the values mentioned are mean values of relative intensity and molecular weight,

the relative intensity values are mentioned in the columns situated just to the right of the corresponding columns of molecular mass m / z; the values of the molecular masses m / z can vary up to

± 1 Da of the average value mentioned in the tables, and

the relative intensity values may vary up to ± 20%, more generally ± 5%, of the average value of relative intensity mentioned in the tables, and -. In Tables 1 to 29 and in the figures, the value m / z is reported, where z represents the electric charge of the ionized species. But, in practice, m / z can be likened to the molecular mass m, with z = 1, since the quantity of proteins that ionize twice (z = + 2), or even 3 times (z = + 3), is negligible, given the sensitivity limit of the measurement techniques.

The values reported in Tables 1 to 29 were obtained by whole cell analysis in an α-cyano-4-hydroxycinnamic acid matrix solution. Specifically, the cells were taken up in PBS wash buffer and then mixed, volume-to-volume, with a solution of α-cyano-4-hydroxycinnamic acid matrix, in said mass spectrometric analysis and not before.

Table 1: Spectrum Analysis of Circulating Human Monocytes

 Table 2: Analysis of the spectrum of circulating human T lymphocytes

intensity m / z (Da) intensity m / z (Da) intensity m / z (Da) (%) (%) (%)

 Table 3: Spectrum Analysis of Human Neutrophil Polynucleotides

 Table 4: Spectrum analysis of human red blood cells

 Table 5: Analysis of the spectrum of human macrophages derived from monocytes

 Table 6: Dendritic cell spectrum analysis differentiated from human monocytes

Table 7: Spectrum Analysis of Murine Macrophages Derived from Bone Marrow

 Table 8: Spectrum Analysis of THPl Cells

Table 9: Spectrum Analysis of J774 Cells

Table 10: Spectrum Analysis of DH82 Cells

 Table 11: Human T-cell Spectrum Analysis

C8166

Table 12: Spectrum analysis of L929 cells

Table 13: HeLa Cell Spectrum Analysis

Table 14: 293T Cell Spectrum Analysis

Table 15: Spectrum Analysis of BeWo Cells

 Table 16: JEG Cell Spectrum Analysis

Table 17: Plasma Trophoblast Spectrum Analysis

Table 18: Spectrum Analysis of XTC-2 Cells

4738 2.58 6526 1.13

 4756 0.92 6557 0.70

 Table 19: Cell spectrum analysis of Acanthamoeba polyphaga

 Table 20: Cell Spectrum Analysis of Acanthamoeba castellanii

 Table 21: Cell Spectrum Analysis & Hartmannella vermiformis

 Table 22: Cell Spectrum Analysis Poteriochromonas melhamensis

intensity m / z (Da) intensity m / z (Da) intensity m / z (Da) (%) (%) (%)

 In another embodiment of the method according to the invention, it is sought to identify a given type of mammalian cells, preferably human, chosen from the main types of circulating immune cells consisting of monocytes, T lymphocytes and polynuclear cells. neutrophils and red blood cells.

In another embodiment, it is also sought to identify B lymphocytes, dendritic cells and so-called "natural killer" cells.

According to other particular characteristics of the process of the invention:

in step 5 /, it is sought to identify a given activation state by a given activation factor of a given type of mammalian cells, and in step 4 /, one of the two steps 4a / and 4b / following is carried out, in which:

in step 4a /, a comparison is made with a reference spectrum of said given type of mammalian cells in a said activation state given by a said given activation factor, or

in step 4b /, said reference spectra database comprises spectra made from purified extracts of different given types of mammalian cells in different given activation states by different given activation factors. More particularly, in step 5b /, an activation state of a given cell type is identified by characterizing its proximity with respect to different activation states by different activation factors of one type. given mammalian cells, by characterizing the proximity of the spectrum obtained in step 3 / with respect to different reference spectra respectively corresponding to the different activation states, by different activation factors, of said given type of cells. by determining the number of peaks in common between the spectrum obtained and that of at least one said reference spectrum. The identification of a given cell type of a sample tested by analysis of the proximity of the mass spectrum obtained with respect to the reference spectra in a said clustering, dendogram or 2D type representation, is more particularly useful for characterizing a type of eukaryotic cells given in a given activation state by a given activation factor relative to different reference activation states by different activation factors.

More particularly, the different activation states given by different activation factors of given types of mammalian cells are activation states of macrophages or circulating human cells, such as monocytes or lymphocytes, activated by a selected cytokine. among IFN--, TGF-1, TNF, IL-4, IL-10 or with a bacterial ligand such as lipopolysaccharide (LPS) or peptidoglycan (PGN).

More particularly, it is sought to identify a given activation state of a human macrophage with respect to activation states resulting from treatments for 18 hours with cytokines selected from IFN--, IL-4, TNF, IL- 10, TGF-1, LPS and PGN, by comparison with the reference spectra comprising the characteristic peaks mentioned in the following Tables 23 to 29, in which the different characteristic peaks are characterized by their coordinates on the abscissa m / z ( Da) and their ordinates of relative intensity expressed as a percentage of the intensity of the peak of greater intensity (%).

Table 23: Spectrum of macrophages treated for 18 hours with 20 ng / ml of IFN- ^■

Table 24: Spectrum of macrophages treated for 18 hours with 10 ng / ml IL-4

 Table 25 Spectrum of macrophages treated for 18 hours with 10 ng / ml TNF

 Table 26: Spectrum of macrophages treated for 18 hours with 10 ng / ml of IL-10

 Table 27: Spectrum of macrophages treated for 18 hours with 10 ng / ml of TGF-1

 Table 28 Spectrum of macrophages treated for 18 hours with 1 μg / ml of LPS

 Table 29: Spectrum of macrophages treated for 18 hours with 10 ng / ml PGN

 The identification method according to the invention makes it possible, in step 5 /, to monitor a given activation by a given activation factor of a given type of mammalian cells, by comparing its spectrum. mass with reference spectra of different activation states by at least one said activation factor of said given type of eukaryotic cells.

In a particular and advantageous embodiment of the method according to the invention, in step 5 /, it is sought to identify a given type of cells consisting of trophoblast cells of human placental tissue, preferably human cells. More particularly still, in step 5 /, the purification is monitored or the progress of the purification of a population of a given type of mammalian cells is monitored by comparing the spectrum obtained from a cell type extract obtained during the purification process, with a said reference spectrum of said given type of eukaryotic cells.

Advantageously, in the process according to the invention, said whole cells, which are analyzed to establish the mass spectrum, were frozen and thawed before establishing said mass spectrum.

Other features and advantages of the present invention will emerge in the light of the examples which follow, with reference to FIGS. 1 to 7, in which:

FIG. 1 illustrates the reproducibility of the spectra of different samples of different cell types obtained in MALDI-TOF mass spectrometry, according to a two-dimensional representation, called 2D, (A = T lymphocytes, B = monocytes and C = neutrophilic neutrophils). In FIG. 1, the position of each point corresponds to a relative intensity, as a percentage of the peak of greater intensity, of two peaks of the spectrum obtained by analysis of the same sample, namely here peak 2 to 2 320 Da (m / z = 2320) on the abscissa and peak 30 on 8 568 Da on the ordinates, which are characteristic peaks found in the three types of spectra of each of the respective cell types concerned; FIG. 2 represents a graph 1 of hierarchical classification (called "clustering") of proximity analysis of the spectra of the following cells: A = T lymphocytes, B = monocytes, C = neutrophils (PMNs) and D = red blood cells (RBCs) ). An upper part 2 corresponds to a so-called dendogram representation of the hierarchical ranking of graph 1 of the four cell types visualizing the distance (inversely proportional to the proximity) between the different cell types. Part 3 corresponds to the molecular weights of the peaks present in at least one of the spectra;

FIG. 3 is a dendogram representation of the proximity of the cell type spectra resulting from a hierarchical classification analysis; the d values from 0 to 1000 on the axis are the "distance" levels between the cell types. concerned. The distances (d) between two cell types are all the smaller as they are close, that is to say they have all the more common characteristic peaks shared in their spectra (and vice versa) ;

FIG. 4 represents, at A, the reference monocyte spectrum of the database and, at B, the spectrum of a new cell population to be compared with a reference spectrum A of the database. In FIG. 4, the intensities are expressed as a ratio of the intensity of the peak / intensity of the peak of greater intensity. An identity between two spectra is determined according to the value of a score, called an identification score, as calculated by the Biotyper 2.0 software, provided by the manufacturer of the MALDI-TOF mass spectrometer, Bruker Daltonics. In the present case, the identification score of 2.65 indicates that the population tested is, presumably, monocytes;

- Figure 5 shows a portion of the spectra in the 2-7 kDa region of monocyte derived macrophages incubated for 18 hours in the presence of IFN ^■, IL-4 and in the absence of activation ( "NS" = Unstimulated macrophages, "IFN--" = "IFN -" and "IL-4" stimulated macrophages = IL-4 stimulated macrophages), the peak intensity being expressed in arbitrary units (ua). The representation of the spectra in FIG. 5 is obtained by the Flexcontrol software which is the management software of the MALDI-TOF mass spectrometer supplied by the manufacturer Bruker Daltonics; FIGS. 5A, 5B and 5C are a comparison of the spectra obtained from macrophages treated with IFN-- ("IFN--") or macrophages treated with IL4 ("IL-4") or macrophages which are not activated or are said to be quiescent (" NS "). FIG. 5A shows the reproducibility of the spectra of four samples of each of the three different types of macrophage activation in a two-dimensional 2D representation, as in FIG. 1, by relating the relative intensities as a percentage of the Peak of greater intensity, characteristic common peaks Nos. 13 to 4939 Da on the abscissa and 14 to 4965 Da on the ordinate, for each of the different types of macrophage activation. In FIG. 5B, a hierarchical proximity classification of the spectra of the different samples of the different types of macrophage activation, in graph 1, showing the characteristic peaks present in the spectrum of the cellular type concerned in the hatched areas, was carried out. said graph being surmounted by a representation in the form of a dendogram 2 visualizing the distance (inversely proportional to the proximity ratio) between the different cell types, as in FIG. 2. With regard to graph 1, part 3 corresponds to the molecular weights of peaks present in at least one of the spectra. In FIG. 5C, the dendogram shows the proximity of the macrophage spectra treated with IFN- and IL-4 at 9 hours, 18 hours, 36 hours and 48 hours. In Figures 5, 5A and 5B, the macrophage samples were run for 18 hours; Figures 6A, 6B and 6C are comparisons of IFN-stimulated macrophages, IL-10, TGF-1, TNF, IL-4 and unstimulated macrophages (NS), respectively. The spectra obtained are compared using different software to obtain a two-dimensional representation, called in 2D, in FIG. 6A to check the reproducibility of the spectra, as in FIG. 1, then in hierarchical classification 1 and dendogram 2 in FIG. 6B and in FIG. Figure 6A shows the two common peaks characteristic of all of the spectra of FIG. all types of macrophage activation tested were peak 17 at 4939 Da on the abscissa and peak 10 at 4 209 Da on the ordinate. In Figure 6C, the macrophages are incubated for 18 hours or 36 hours in the presence of the different cytokines. In Figures 6A and 6B, the macrophages were incubated by the subject cytokines for 18 hours;

Figures 7A and 7B show comparisons of LPS and PGN stimulated macrophages with unstimulated macrophages (NS). The macrophages are incubated for 18 hours in the presence of LPS or PGN. FIG. 7A is a representation called in 2D and FIG. 7B is a hierarchical classification representation in the form of graph 1 and dendogram 2, the peaks being classified in an order explained below with reference to FIG. 7A, to verify the reproducibility of the spectra of the different types of activation of macrophages concerned, the intensities of the characteristic common peaks Nos. 6 to 4,209 Da on the abscissa and peak No. 9 were measured at 4,748 Da on the ordinate of the spectra of the said spectra. samples tested.

In FIGS. 1, 5A, 6A and 7A, the peak intensities are expressed in relative intensities, expressed as a percentage of the intensity of the peak of greater intensity.

The inventors have set up an experimental protocol in order to identify different cell types within heterogeneous samples, for example by taking the circulating immune cells as a first example and for example the cells constituting a complex solid tissue, the placenta. MALDI-TOF mass spectrometry can thus make it possible to study the presence of different populations within the tissues of higher organisms.

EXAMPLE 1 MALDI-TOF Mass Spectrometry Analysis of Circulating Leukocytes 1.1 Isolation of Circulating Leukocytes The blood of healthy volunteers (French Blood Establishment, Leukopacks) is diluted in 0.9% NaCl and deposited on a Ficoll density barrier (MSL, Eurobio), which allows, after centrifugation, to obtain on the one hand, the cell population with a density of less than 1.077, that is to say mononuclear cells, and, on the other hand, cells with a density greater than 1.077, consisting of neutrophils and red blood cells. The mononuclear cells consisting essentially of monocytes and T cells are recovered and suspended in RPMI 1640 buffered with 20 mM Hepes (Invitrogen). Monocytes and T cells are then isolated by positive selection using magnetic beads coated with antibodies directed against the CD14 (monocytes) and CD3 (T cells) (Miltenyi Biotec) molecules. The quality of the CD14 + or CD3 + cell preparations is checked in flow cytometry (in general, more than 98%). Their viability evaluated by exclusion of trypan blue is greater than 95%. The neutrophils are isolated from red blood cells after a 30 min incubation in 1.5% Dextran T-500.

1.2 - MALDI-TOF mass spectrometry on whole cells After counting the cells, their concentration is adjusted to

10 ⁶ / ml in phosphate buffer (PBS). The Eppendorf tubes containing 1 ml of cells are centrifuged at 300 xg for 5 min and the cell pellet is taken up in 10 μl of sterile PBS and then frozen at -80 ° C for 48 hours. MALDI-TOF mass spectrometry analysis is performed using the AutoflexII mass spectrometer and FlexControl software (Bruker Daltonics). After gentle thawing of the samples, 1 μL of the cell suspension is deposited on the MALDI target on which 1 μL of the α-cyano-4-hydroxy-cynnamic acid (HCCA) matrix is added. Evaporation takes place at room temperature and the molecules of the matrix contain those of the sample in the form of crystals. In the ionization chamber, these crystals are subjected to a radiation of 5 ns of the laser adjusted to 337 nm and the ionized molecules released are analyzed thanks to their flight time. The FlexControl software provided by Bruker Daltonics essentially allows the acquisition of spectra from the raw data.

1.2.1 - Analysis of the Leucocyte Spectrum In a first series of experiments, the inventors analyzed the spectrum in MALDI-TOF mass spectrometry of the leucocytes isolated from a blood donor and frozen. There is a set of peaks whose location and relative intensity can be noted whereas, when the deposited sample does not contain cells, no peak is observed.

The position and relative intensity of the 70 most important peaks of each test are taken into account to create a "cell type" reference by the mass spectrometer. This choice of 70 peaks is explained for reasons of efficiency of the evolutionary databank that the inventors have put in place.

To differentiate cell type A from cell type B, in theory only two well chosen peaks are needed. But, to differentiate cell type C from type A (or B), these two peaks are not enough: we must therefore add to them a third peak which, in turn, is insufficient to differentiate C from cell type D etc. in other words, we could perhaps be content with 50 or even 30 peaks to differentiate the 22 cell types present in the current database but it would limit its power of differentiation of new cell types. Since each spectrum contains between 80 and 100 peaks, it was necessary for the inventors to choose between the maximum number of peaks representative of a given cell type and a too large number for which they would not have data: 70 peaks appeared to them a a reasonable number since it allows to acquire all data regardless of the cell type studied and gives the database its maximum power. The average value and the relative intensity of the representative peaks of monocytes are given in Table 1. We thus observe a major peak at 4960 Da (intensity arbitrarily adjusted to 100%) and many minor peaks whose relative intensity is calculated in percentage of the peak of greater intensity at 4960 Da. Some of them have an intensity that represents about 20% of the intensity of the main peak while others represent less than 1% of this intensity.

For the other cell populations of circulating cells, the results are reported in Tables 2 (T lymphocytes), 3 (neutrophils) and 4 (red blood cells).

The inventors verified whether the conditioning of the cells affects their profile in MALDI-TOF mass spectrometry. It appears that the lysis and sonication of the cells before freezing profoundly alter their profile. They also studied the MALDI-TOF mass spectrometry profile of unfrozen cells. This is, in contrast, in all respects comparable to that obtained on frozen cells, which has two implications: 1. the peaks detected in MALDI-TOF mass spectrometry on frozen cells correspond to a biological reality since they are also characteristics of living cells; 2. Freezing at -80 ° C for 48 hours is a simple and reproducible method of analyzing cell populations.

The inventors have also determined the concentration of cells necessary to define a reproducible signature, the objective to be achieved is to reduce this optimal concentration in order to work on small clinical samples. A concentration of 5 x 10 ⁵ to 10 ⁶ cells in 10 μl of PBS provides an optimal resolution: a concentration of 5 x 10 ⁶ cells / 10 μl increases the background without revealing new peaks and a concentration of 10. ⁵ cells / 10 pL does not reveal all the peaks. This is the reason why the inventors adopted a concentration of 10 ⁶ cells in 10 μl of PBS for all the experiments described below.

The location and relative intensity of the peaks obtained are specific to the cell populations and remain constant from one sample to another and from one blood donor to another (8 different donors tested).

1.2.2- Reproducibility and specificity of the spectra of the different cell types (Figure 1)

The inventors verified this reproducibility by a two-dimensional representation, called 2D, obtained with the ClinProTools software (version 2.2) from spectra of 8 samples of different blood donors. They found that the MALDI-TOF mass spectrometry signatures, for two peaks arbitrarily selected by the mass spectrometer, are remarkably homogeneous within each leucocyte type, as shown in Figure 1.

More specifically, in FIG. 1, the T cells (A), the monocytes (B) and the neutrophils (C) (10 ⁵ cells of each cell type by 1-I deposition) were isolated from 8 blood donors. different, whose blood samples were frozen in 10 μl of PBS before being thawed. Then 1 μΙ is deposited on the target of the MALDI-TOF mass spectrometer.

The software analyzes only two common characteristic peaks considered to be the most important (that is to say, the best separated and of the highest relative intensity possible for at least one of the peaks considered) found in the different spectra. different samples of the different cell types studied. The two peaks are chosen by also verifying that we find the same difference with the peak that precedes and the peak that follows in the different spectra of the tested samples of the different cell types. In practice, it is positioned identically in the different spectra at ± 1 Da. The fact that the relative intensity differences for these two peaks remain in the limit of variation around a mean value of ± 20% observed between the different spectra of the same cell type demonstrates the significant reproducibility of the spectra. In FIG. 1, the spectra obtained are analyzed with the software

ClinProTool (version 2.2) which automatically selects 2 peaks whose position is represented in 2D form (one peak on the x axis and the other on the y axis) by assigning them a number (the number 1 being assigned to peak of the lowest molecular weight and so on) with the corresponding molecular weight. This is the peak 2 of 2320 Da (x-axis) and the peak 30 of 8568 Da (y-axis). The position of each point of the graph thus corresponds to the relative intensity of the two peaks of each sample. A point at the bottom left indicates that the 2 peaks that characterize the sample are absent or weakly present (here, monocytes). A point down and to the right means that peak # 2 is present and intense but peak # 30 is not intense (here, neutrophils). A point at the top left indicates that peak # 30 is present and intense but peak # 2 is not intense (here, T cells). The proximity of the samples to each other suggests that they are likely to belong (only 2 peaks are analyzed and not all 70 peaks) to the same cell type while their distance shows that they belong to different cell types. It can be deduced from FIG. 1 that the spectra obtained from various samples of a given cell type are close to one another and that the spectra of T lymphocytes (A), monocytes (B) and neutrophils (C) exhibit different characteristics.

1.2.3 - Specificity of leukocyte profile in MALDI-TOF mass spectrometry

The inventors wanted to establish whether the leukocyte profile in MALDI-TOF mass spectrometry is closely specific or representative of a class of circulating cells. For this, they compared the MALDI-TOF mass spectrometry profile of monocytes with those of three other populations of circulating cells, T lymphocytes (Table 2), neutrophils (Table 3) and red blood cells (Table 4). Some peaks present in monocytes are absent from T lymphocytes, others are present only in T lymphocytes and some are common to both. Finally, the relative intensity of a peak may be different in monocytes and T lymphocytes. The same observation applies to neutrophils. The latter are characterized by peaks distinct from those of lymphocytes and monocytes even though some peaks are expressed by the three cell types. Finally, it can be seen that the spectrum of red blood cells is vastly different from that of the three leukocyte populations (see Figures 2 and 3).

1.2.3.1- Hierarchical ranking

The inventors have analyzed the distances or proximities between the different spectra by grouping or hierarchical classification, called "clustering", by adapting the software MultiExperiment Viewer (MeV) 4.3. This free and open source software is available, among others, at http://www.tm4.org/mev/. It is conventionally used to analyze transcriptomic data in "microarray" (microarray). In FIG. 2, this analysis reveals important differences between the main types of circulating cells (T lymphocytes, monocytes, neutrophils and red blood cells) and gives a representation of these differences in the form of a dendogram (FIG. 2).

More precisely, in FIG. 2, the T (A), monocyte (B), neutrophil (C) and red blood cell (D) cells were from 8 different blood donors, whose blood samples were frozen, then thawed and deposited on the target of the MALDI-TOF mass spectrometer (10 ⁵ cells by 1-I deposition).

The molecular weight values of the different peaks are manually recorded on an Excel sheet and saved in text (.txt) format. Values +1 (shaded area) or -1 (empty area) are assigned to them depending on whether the peak is present (+1) or absent (-1) on the spectrum. This data is loaded into the software. The data of each of the 4 cell types are automatically compared by the software MeV 4.3, which allows to establish a hierarchical classification between the 4 cell types.

In the graph 1 of Figure 2, on the right column 3, the peaks are classified in an apparent disorder, with respect to their molecular weight. Peaks present are grouped in a shaded area for each cell type as follows. The peaks are classified in successive groups: the software includes for a given cell type the successive peaks and groups them as long as these peaks are specific for this cell type. Thus, for cell type C, the peaks of 2138 to 13164 Da are grouped together. When peaks are common between cell types C and B, for our example, it places them in continuity for C and contiguous for B. These peaks are again ranged from the lowest to the highest molecular weight, in particular here, from 5,418 to 10,838 Da. As the software finds specific peaks of B, it places them in continuity of the peaks from 5,418 to 10,838 Da, which goes from 2,537 to 14,697 Da. Thus he also leaves, for D, from 4,284 to 15,873 Da or for A from 2,162 to 15,885 Da. It is therefore seen that the overlaps govern the graphical presentation with as much respect as possible of the increasing order of the molecular weights.

1.2.3.2- Dendogram (Figure 2)

The Mev 4.3 software is conventionally used for the analysis of proximities or distances of transcriptomic data by dendogram.

"Clustering" highlights the specific signatures of the different cell types and the resulting dendogram representation makes it possible to better visualize the relative "distance" between several cell types. The upper part 4 of FIG. 2 corresponds to a dendogram representation of the four cell types. In a dendogram, the horizontal lines indicate that the connected cell types have a certain percentage of peaks in common, this percentage being all the greater as the vertical lines below the horizontal line connecting these cell types are small. In this case, the two closest cell types, that is to say having the greatest number of peaks in common, are monocytes (B) and neutrophils (C). The remoteness of a horizontal line, that is to say the height of the vertical lines delimiting the horizontal line, accounts for the "distance" between the cellular types concerned, being inversely proportional to the number of peaks in common. It is thus seen that the red blood cells (D) are farther away from the neutrophils (C) than from the monocyte (B) -lymphocytes T (A) group. The dendrogram representation that frees itself from the representation in hierarchical classification is directly derived from the data of the hierarchical classification.

The classic method of determining a hierarchical "clustering" is explained on the website http: //asi.insa- rouen.fr/enseignement/siteUV/dm/Cours/clustering. pdf and in the work of Husson F., Lê S, Pages J. Practical statistics, 2009, pages 169-202, Chapter 4: Classification, editions Presses Universitaires de Rennes. In this case, all the peaks present in any of the four spectra are counted. They would be 280 if the four spectra had no peak in common. The number of common peaks between the different spectra is then calculated and related to the total number of peaks of the 4 types of spectra of the 4 cell types. The two spectra that have the highest percentage of common peaks are considered the "closest", namely here B and C. Hierarchical "clustering" consists of an ascending hierarchical classification for which each "cluster" is progressively "absorbed". "by the nearest" cluster "to form an anchor cluster to be compared with others cell types. Thus, the spectrum A and the spectrum D are compared with the anchoring cluster B + C. It is observed that the spectrum A is "closer" to the set B + C than the spectrum D. The set B + C is therefore connected by a vertical bar to a horizontal bar connecting A to B + C. The height separating the horizontal bar connecting A to the set B + C is larger than that of the horizontal bar connecting B and C. The set A + B + C which results is compared to D and we see that the vertical bar which links D to the set A + B + C has a greater height than the others, which means that the spectrum D is the least "near" spectra A, B and C. This representation by successive fusions constitutes a dendogram. Two "clusters" deemed "close" in the dendogram show that they share the maximum of peaks in common when compared to other "clusters". Their gradual removal means that they share fewer and fewer peaks in common with other clusters. Here, the spectra B and C are therefore considered to be the "closest", the spectrum A being more "distant" from B and C and the spectrum D being the one that shares the least number of peaks in common with the spectra A, B and vs.

Example 2: Realization of a database of 22 cell types

2.1 - Obtaining the data bank

The fact that the spectra obtained on circulating cells are highly reproducible and specific for a given cell type has allowed the inventors to extend their study to a larger number of eukaryotic cell types.

The inventors have thus chosen to differentiate between human monocytes and macrophages (Table 5) by a 7-day culture in medium containing human serum AB or in dendritic cells (Table 6) by cultivating them for 5 days in a medium containing Granulocyte Macrophage. Colony Stimulating Factor and Interleukin (IL) -4. They also used mouse macrophages (Table 7) differentiated from their precursors present in the bone marrow by culturing in the presence of a medium rich in Macrophage Stimulating Factor as well as established lines of macrophage-like cells such as the human line THP-1 (ATCC No. TIB-202, Table 8), murine line J774 (ATCC No. TIB-67, Table 9) and the canine line DH82 (ATCC No. CRL-10389, Table 10).

They also used the C8166 human T cell line which stably expresses the CCR5 receptor Table 11), the L929 mouse fibroblast line (ATCC No. CCL-1, Table 12), the epithelial cell lines of human origin. HeLa (ATCC No. CCL-2, Table 13) and 293T (ATCC No. CRL-1573, Table 14), human trophoblastic lines BeWo (ATCC No. CCL-98, Table 15) and JEG (ATCC No. HTB- 36, Table 16) that they compared to human placental trophoblasts (Table 17). Finally, they used non-mammalian cells such as the XTC-2 line derived from Xenopus laevis (Table 18) previously described (Drancourt M, Jarlier V, Raoult D. The environmental pathogen Mycobacterium ulcerans grows in amphibian cells at low temperatures. About Microbiol 2002, 68: 6403-6404) and different types of amoebae such as Acanthamoeba polyphaga (ATCC No. 30461, Table 19), Acanthamoeba castellanii (ATCC No. 30234, Table 20), Hartmannella vermiformis (ATCC No. 50237, Table 21) and Poteriochromonas melhamensis (ATCC No. 11532, Table 22).

The 22 cell types entered into the database represent a large sample of eukaryotic cells thus comprising mammalian and non-mammalian cells, primary cells, differentiated cells of primary cells and immortalized cells in lineage. Their characteristics, corresponding to the different cell types whose characteristic peaks are described in Tables 1 to 22, are summarized in the following Table A: Cell Type / Table Nature Peak Characteristics

Monocytes Primary cells Human circulating cells / Table 1

T cells Primary cells Human circulating cells / Table 2

Polynuclear Primary Cells Circulating Neutrophil Cells

humans / table 3

Hematases Primary cells Human circulating cells / Table 4

Macrophages Primary differentiated cells of humans / Table 5 monocytes

Macrophages Primary differentiated cells of murine marrow / bone chart 7

THP-1 / Table 8 Myelomonocytic human line

J774 / Table 9 Murine line Macrophage immature

DH82 / table 10 Macrophage canine line

Cells Differential Primary Cells of Dendritics / Table 6 Monocytes

Human T lymphocytes Human line Lymphocytes

C8166 / table 11 expressing CCR5

L929 / Table 12 Murine line Fibroblastic type

HeLA / Table 13 Human line Epithelial type 293T / table 14 Human line Epithelial type

JEG / table 16 Human line Trophoblastic type

BeWo / Table 15 Human line Trophoblastic type

Trophoblasts Primary cells Human placental cells / table 17

XTC-2 / table 18 Non-Xenopus laevis line

 mammal

Acanthamoeba Non Amoeba Organism

 po / yphaga / tab \ water 19 mammal

Acanthamoeba Non Amoeba Organism

Mammal

Hartmanella Non Amoeba Organism

 verm / form / s / tab \ water 21 mammal

Poteriochromonas Non Amoeba Organism

me / hamens / s / tab \ water 22 mammal

To make a database from these 22 cell types, the inventors again used the BioTyper version 2.0 software. For each cell type, at least 20 individual spectra were made, which automatically created a mean spectrum containing 70 peaks for the 22 cell types above, as previously described in Tables 1 to 22.

Hierarchical classification analysis (not shown) shows that there are significant differences between the 22 cell types used but the resulting representation is difficult to read and interpret. In contrast, the dendrogram representation of the same data (Figure 3) is much more explicit and reveals important differences between the 22 cell types entered in the database. data. The 22 cell types (10 ⁶ cells in 10 μl of PBS) were frozen, then thawed and 1 μl was applied to the MALDI-TOF mass spectrometer target. Figure 3 is a dendogram representation of the proximity of cell type spectra resulting from hierarchical classification analysis, the d values from 0 to 1000 on the axis are the distance levels between the cell types involved.

Two "clusters" such as the JEG and Bewo cells are "close" to each other (on a scale of 0 to 1,000). Their merging leads to an anchoring "cluster" that makes it possible to compare it to the "cluster" consisting of the HeLa and 293T cells, which is therefore relatively close to it. The new "cluster" anchor shows that it is a little further away from the "cluster" L929 cells. The analysis continues and shows that the "anchorage cluster" consisting of neutrophils, monocytes and T cells is far from the "anchorage cluster" of J774 cells at 293T.

Figure 3 confirms that: a. each cell type tested has a signature in MALDI-TOF mass spectrometry of its own, b. it will be possible to integrate new cell types into the database.

2.2 - Resolving power of the data bank

2.2.1 - Principle

The interest of a data bank is to be able to compare a new datum (here, the spectrum of a new cell sample) with the representative spectra of each cell type present in the database. The inventors compared a new spectrum with the average spectra contained in the database thanks to the Biotyper 2.0 software developed by the manufacturer of the MALDI-TOF mass spectrometer (Bruker Daltonics). This results in validation scores which represent the addition of 2 notes ni + n2 (and being a function of the number of peaks and n2 function of the signal / background noise ratio).

- nor is the score given according to the number of characteristic peaks of the spectrum n, with

. ni = 0 for n <8. ni = 1 for 8 <n <27

. ni = 2 for 27 <n <43

. ni = 3 for 43 <n <78

. ni = 4 for 78 <n <86

. ni = 5 for 86 <n <110. ni = 6 for 110 <n, and

n2 is the score given according to the value of the signal-to-noise ratio (Rmax) of the peak of greater intensity of the spectrum, with

. n2 = 0 for Rmax <6.8

. n2 = 1 for 6.8 <Rmax <26.8. n2 = 2 for 26.8 <Rmax <52.6

. n2 = 3 for 52.6 <Rmax <208.6

. n2 = 4 for 208.2 <Rmax <398.6

. n2 = 5 for 398.6 <Rmax <1092.2 n2 = 6 for 1092.2 <Rmax. A spectrum is taken into account if its validation score (nl + nl) is greater than 2.

When comparing a new spectrum with average reference spectra present in the database, another score, called an identification score, is calculated by the Biotyper 2.0 software. The algorithms for calculating these scores are described in the manual provided by Bruker Daltonics, which gives a brief example of the method used in his manual "Software for microorganism identification and classification". Thus, an identical peak between the database and the tested cell type gets a positive score while the absence of a peak results in a negative score. The set of scores obtained is expressed in logarithmic scale. Scores obtained to identify and classify microorganisms (Fournier PE, Couderc C, Buffet S, Flaudrops C, Raoult D. Rapid and cost-effective identification of Bartonella species using mass spectrometry, J Med Microbiol 2009, 58: 1154-1159) are those that the inventors have used to identify and classify eukaryotic cells. Thus, an identification score between 0 and 1.699 makes it possible to exclude the new population as belonging to the cellular type present in the database. An identification score between 1,700 and 1,999 suggests that the new population belongs to the cell type present in the database and an identification score greater than 2.0 allows for a certain identification of the new population as belonging to the cell type present. in the database. In the future, extending the database to a larger number of cell types could lead to a higher identification threshold.

BioTyper 2.0 software identifies unknown spectra by comparison with reference spectra stored in a database. The identification results are given in the form of a graphical representation, as in FIG. 4, with an identification score calculated as hereinafter explained. At first, the spectra of two samples considered identical are aligned. An acceptable error is assigned for the peak mass of each spectrum (± 1 Da) and the software repositions the two spectra (in other words, if the difference between the peaks of the same spectrum and the peaks of the other spectrum is too much different, the software will not be able to align the spectra).

In a second step, the comparison of an unknown spectrum with the reference spectrum (namely here, the average spectrum present in the database) is evaluated using dedicated identification scores. The algorithms for calculating these scores are described in the manual provided by Bruker Daltonics, which gives a brief example of the method used in his manual "Software for microorganism identification and classification". For this, the information from the analysis of the reference spectrum is converted into a value called maximum score accessible ("maximum score or Max Se") calculated as follows: each peak present with a frequency of 100% gets 100 points; the peaks present with a lower frequency obtain a number of points equivalent to their frequency (for example, a small peak present in only 8 spectra present in the database which contains 20 has a score of 8/20 = 40). The addition of all points gives the maximum score accessible. Thus, for example, a reference spectrum that contains 10 peaks with 100% frequency, 5 peaks with a frequency of 50% and 3 peaks with a frequency of 10% reaches a maximum accessible score of (10xl00) + (5x50) + (3 × 10) = 1 280.

Once this score is established for the reference spectra, each peak of the spectrum to be analyzed is compared to the same peak of the reference spectra. For this, two windows of adjustable masses ("adjustable mass Windows") are created. The window "inner mass window" corresponds to perfectly adjusted peaks, which gives them a full score and the window "outer mass window" corresponds to peaks not perfectly aligned: the value of the score attributed is proportional to the distance with the "corresponding" peak of the reference spectrum (if Figure 4 was in color, we could see in green the perfectly adjusted peaks between the reference peaks and the peaks of the tested sample, in yellow the peaks partially adjusted and red unadjusted peaks). This results in a cumulative score for the sample to be compared to the reference spectrum. The comparison of this cumulative score ("actual score or Act Se") with the maximum accessible score ("Max Se") leads to a relative score ("Rel Se") or Rel. Sc. = Act. Se. / Max. Se. (with a maximum of 1 for a perfect identity). The final score ("overall score") also includes the number of perfectly adjusted peaks (PN k), the number of partially adjusted peaks (PN b) and the total number of peaks (PN m) present in the spectrum. in the form of a relative number of peaks ("P-Number Rel.") which is calculated as follows: Rel. P-Num. = (PN k) + (0.5xPN b) / (PN m). Finally, a correlation calculation ("Effective I-correlation value" or I-Corr.) Is performed. The final score is calculated as follows: Rel. Se. x Rel. P-Num. x I-Corr. x 1000 (with a maximum of 1000) then expressed in Log (the scale goes from 1 = no homology to 3 = perfect identity). This is how scores <or> are obtained at 2.0. 2.2.2 - Applications to circulating cells

The inventors have thus compared a new sample of purified monocytes with the reference "monocyte" of the databank: the score of 2.65 authenticates the population tested as monocytes (FIG. 4). They then tested the discriminating power of the databank in three different ways.

They mixed T lymphocytes and monocytes (5 × 10 ⁵ T cells + 5 × 10 ⁵ monocytes taken up in 10 μl of PBS) equally in order to analyze the resulting spectrum. They have identified with certainty, thanks to the comparison of the spectrum obtained with the references in the database, both monocytes and T cells (score of 2.25 in both cases).

- Peripheral blood mononuclear cells (PBMCs), obtained directly after the Ficoll gradient, consist essentially of monocytes and T lymphocytes in a proportion of 1 to 7. The comparison of the spectrum of these mononucleated cells (10 ⁶ PBMCs taken up in 10 .mu.l of PBS) with the spectra present in the database makes it possible to identify both the monocytes (with an identification score of 2.08) and the T lymphocytes (with an identification score of 2, 02).

- The effectiveness of the database was also studied on whole blood containing all the figured elements. The presence of red blood cells obscures the identification of other cell types. The inventors have therefore subjected a blood sample to a brief hypotonic shock which makes it possible to preserve the leucocytes, but not the red blood cells. Under these conditions, they only definitively identified neutrophils (with an identification score of 2.049).

Conclusion

From a practical point of view, these results show that, for the management of blood samples, MALDI-TOF mass spectrometry can identify T lymphocytes and circulating monocytes once they are purified as mononucleated cells. . The neutrophils can be studied directly from whole blood after an osmotic lysis phase. Example 3 Identification of Activation Profiles of Macrophages

3.1 - Notion of activation of macrophages

Macrophages result from the differentiation of circulating monocytes when they reach tissues. They are at the heart of the answer immune: they are likely to destroy infectious agents or tumor cells; they influence the response of lymphocytes which, in turn, control their activity. The microbicidal and tumoricidal functions of macrophages are controlled by their microenvironment. It has been proposed to call M1 macrophages fully microbicidal and tumoricidal (macrophages responding to a cytokine such as interferon (IFN) -) and macrophages M2 macrophages that respond by an alternate pathway (response to interleukin (IL -4-, IL-10 or Transforming Growth Factor (TGF) - ^■■ The functional properties of these macrophages are quite diverse since they can be of little or no microbicide and can modulate the immune response of more or less effective It is considered that there is no real M2 state but that it is a continuum of states.It is thus understood that their deficiency or hyperactivity places them at the heart of pathologies. infectious, tumoral or inflammatory.This is also to say that the characterization of the activation state of macrophages is important to better classify pathologies, to appreciate the response to treatments and to have a prognostic approach 3.2 - Study of the act ivation of macrophages

There are no specific markers of a given state of activation of macrophages. The M1 / M2 classification leads to the identification of M1 or M2 macrophages according to the expression of a set of detectable markers in flow cytometry, ELISA technique, or even in RT-PCR or "microarray". None of these markers, however, allows an absolute identification.

In view of the difficulty of identifying the different activation states of macrophages and the importance of these activation states in human pathology, the inventors, in view of the results obtained in MALDI-TOF mass spectrometry on circulating cells, have wondered whether such an approach allows the characterization of macrophage activation profiles, or even their classification into macrophages Ml or M2. They studied macrophage activation patterns in response to various stimuli.

3.3 - Activation protocol of macrophages

The monocytes (10 ⁶ cells in a 6-well plate) are incubated for 4 days in 3 ml of RPMI 1640 containing 20 mM HEPES, 2 mM of L-glutamine and 10% of human serum AB and then 3 additional days in medium containing 10% of SVF. The purity of the macrophages (> 90%) is verified by flow cytometry using CD68 as a specific marker. The macrophages are then stimulated by different cytokines such as IFN- (TebuBio) at 20 ng / ml, IL-4, IL-10, TGF-1 (10 ng / ml, R & D Systems) or Tumor Necrosis Factor (TNF, 10 ng / ml, Euromedex). In some experiments, macrophages are stimulated with 1 μg / ml lipopolysaccharide (LPS) prepared from Escherichia coli or 10 ng / ml peptidoglycan (PGN) prepared from Ba cillus subtilis (Sigma-Aldrich).

3.4 - Analysis of macrophage spectra according to their activation state

3.4.1 - Analysis of the spectra of human macrophages derived from monocytes - The inventors first analyzed the spectrum of macrophages obtained after 7 days of culture. Numerous peaks in the molecular weight range from 2 to 20 kDa are observed (see Table 5). The spectrum of quiescent macrophages is highly reproducible since the spectra of macrophages differentiated from 5 blood donors are perfectly superimposable.

- In a second time, they studied the stability of spectra over time. For this, they incubated the differentiated macrophages after 7 days for 9, 18, 36 and 48 hours to allow comparison with IFN-or IL-4 activated macrophages. The spectra obtained are identical regardless of the time additional incubation of macrophages, which allows the comparison of different modes of activation of macrophages.

3.4.2 Analysis of IFN-or IL-4-Activated Macrophage Spectra The inventors activated the macrophages for 18 hours with one part of the IFN (Table 23) known to induce a profile Ml and on the other hand of IL-4 (Table 24) known, it, to induce a polarization M2. The doses of IFN- and IL-4 are those that have been used in the laboratory to study the transcriptional profile of M1 macrophages and M2 macrophages. When comparing the macrophage spectra incubated in the presence of IFN- or IL-4 for 18 hours with that of the quiescent macrophages (see Table 5), there are important differences concerning their molecular mass and / or or their relative abundance. The inventors then evaluated the reproducibility and the specificity of the spectra using the ClinProTools 2.2 software as used in example 1. For this, they created for two spectra representative of each state of the macrophages a two-dimensional representation. It is observed that the spectra are characteristic of a given state of macrophage activation since they are homogeneous within the same activation state and that they are specific to each of these states (FIG. 5A). In addition, IL-4-activated macrophages are opposite to IFN-activated macrophages compared to quiescent macrophages, suggesting that MALDI-TOF mass spectrometry may be useful in comparison of IL-4-activated macrophages. macrophages M1 / M2. This specific signature is confirmed by a hierarchical "clustering" classification created by the use of the MeV 4.3 software according to the same procedure used in Example 1 (FIG. 5B). This hierarchical classification makes it possible, on the one hand, to highlight the difference between the three types of macrophages and on the other hand that the quiescent macrophages are closer to the macrophages activated by IL-4 than they are to activated macrophages. by the IFN--. These Specific signatures finally allowed the inventors to establish a dendogram using the same procedure used in Example 1 (Figure 5C). The "divergence" of macrophages treated with IFN- or IL-4 has been studied over time (Figure 5C, see also Figure 6C). The dendogram contains two specific branches that combine activation with IFN-- on the one hand and with IL-4 on the other hand. Activation at 18 hours shows significant differences, is optimal at 36 hours and then decreases. This is the reason why the inventors considered only the two times of 18 and 36 hours in the following studies. These results therefore show that the polarization of macrophages in macrophages M1 or M2 can be studied by MALDI-TOF mass spectrometry.

3.4.3 - Analysis of the Spectra of Macrophages Activated by Different Cytokines The inventors have wondered whether cytokines known to modulate the response of macrophages, such as TNF, IL-10 and TGF-1, induce a profile in vitro. Specific MALDI-TOF mass spectrometry and if this profile corresponds to the M1 and M2 profiles induced respectively by IFN- and IL-4. The location and relative intensity of the peaks induced by TNF, IL-10 and TGF-1 are given in Tables 25, 26 and 27, respectively.

The 2D representation of the spectral analysis shows that IL-10 induces a signature relatively close to that of quiescent or IFN-activated macrophages (FIG. 6A). TNF and TGF - 1 induce signatures close to each other and are not very far from the signature induced by IL - 4. The hierarchical classification representation confirms this visual impression (Figure 6B). There is only one specific peak of IL-10 activation (at 8396 Da), although the general pattern of the IL-10 response is very specific. Two peaks are specific for TGF - 1 (at 2886 and 2318 Da) while other peaks form the specific signature of TNF. The dendogram resulting from the comparison of the different inductors of the macrophage response has two major branches (Figure 6C). The first class ("cluster") groups together IL-10 and IFN--. It may be noted that the "distance" between IFN - and IL-10 seems to depend essentially on the 18 or 36 hour incubation time. The second class includes IL-4, TGF-1 and TNF. Again, the "distance" between TGF - 1 and TNF seems to depend on the incubation time. These results show that macrophages stimulated by cytokines that modulate macrophage response have different profiles and specific signatures in MALDI-TOF mass spectrometry and that they can be classified into M1 or M2 profile.

3.4.4 - Analysis of macrophage spectra activated by different bacterial ligands, LPS (lipopolysaccharides) and PGN (peptidoglycans)

The inventors have finally wondered whether the interaction between certain bacterial ligands and macrophages determines a specific spectral profile and whether these profiles are related to the Ml or M2 responses. They thus analyzed the response of macrophages to LPS, present on the surface of gram-negative bacteria (Table 28) and PGN present on the surface of gram-positive bacteria (Table 29). Spectral analysis shows that some peaks are specific for macrophage response to PGN while other peaks are specific for macrophage response to LPS. The 2D representation of the spectral analysis shows that the LPS induces a signature that is relatively close to that of the quiescent macrophages whereas the PGN induces a signature that is far more distant (FIG. 7A). These signatures are also reproducible from one blood donor to another (here, 4 different donors). The hierarchical classification representation (Figure 7B) reveals that the response of macrophages to LPS is localized on the same branch as the response to IFN-- (M1 type profile) and that the response of macrophages to PGN is "close "of their response to the IL-4 (M2 type profile). These results show that MALDI-TOF mass spectrometry makes it possible to identify different activation states of macrophages. It is therefore possible by a simple, reproducible, inexpensive hardware and easy to implement to know the functional state of the cells present in a normal or pathological tissue. This knowledge can be a valuable aid to the diagnosis and / or prognosis of human diseases, especially infectious diseases.

Example 4: Follow-up of placental trophoblast isolation processes The placenta is a complex tissue in perpetual evolution. It consists essentially of trophoblasts and, to a lesser degree, macrophages and dendritic cells whose precise role is not known. The trophoblast corresponds to the continuous cellular layer of fibroblasts which limits the egg, which becomes blastocyst at the 6th day after fertilization. During implantation, the trophoblast differentiates into two layers, the syncytiotrophoblast and the cytotrophoblast. Placental macrophages are fetal macrophages that reside in the mesenchyme of the placenta.

4.1 - Isolation of different types of placental trophoblasts Fresh placentas are collected after vaginal delivery or from caesarean sections from non-pathological pregnancies and after obtaining consent from patients. Samples are placed on a sterile surface by placing the umbilical cord down and the basal face up. The basal part that corresponds to the decidua is removed from the villous placenta. Large sections of about 30 grams are made in the villous placenta, placed in 50 ml sterile tubes after different washings with sterile PBS and weighed. The tissue is then thoroughly rinsed with PBS to remove as much blood as possible and cut into fragments of about 1 mm ³ . The placenta fragments are incubated in a digestion solution (Hank's buffered saline solution containing 2.5% trypsin / EDTA, DNAse I (100 U / ml), 4.2 mM MgSO 4 and 25 mM HEPES) on a rotary shaker for 45 min at 37 ° C. The dissociated cells are recovered and the remaining tissue fragments are incubated two more times in digestion solution for 30 min at 37 ° C. The whole of the dissociated cells is collected, centrifuged, enumerated and deposited on a discontinuous gradient of Percoll (25 and 60%) and then centrifuged for 20 minutes at 1200 × g. The cell ring obtained at the interface of the two phases, enriched in trophoblasts, is recovered and washed in PBS. The trophoblasts are then selected using magnetic beads coupled to an anti-mouse immunoglobulin antibody (Miltenyi Biotec) and then coated with mouse antibodies directed against the EGF-R molecule ("epidermal growth factor receptor"), a receptor expressed at the surface of the trophoblasts. Part of the cells obtained after passage through a Percoll gradient, selection by anti-EGF-R antibody is stored, frozen (10 ⁶ cells in 10 μl PBS) and then tested in MALDI-TOF mass spectrometry.

4.2 - Follow-up of the steps of purification of placental trophoblasts by MALDI-TOF mass spectrometry The general principle is given in the paragraph "Resolving power of the database" in Example 2, paragraph 2.1.

The inventors compared a sample of cells recovered after Percoll gradient passage with the average spectrum of primary trophoblasts (see Table 17). The validation score calculated using the Biotyper 2.0 software is 2.14, which shows that it is possible to detect the "trophoblast" type in a heterogeneous sample. MALDI-TOF mass spectrometry thus shows that it may be useful for the characterization of a cell type during its isolation out of any prior marking.

Claims

claims

1. A method for identifying a given type of mammalian cells in a biological sample to be analyzed, capable of containing mammalian cells, characterized in that the following steps are carried out in which:

1 / at least one reference spectrum is established by mass spectrometry, called MALDI-TOF ("matrix-assisted-laser-desorption-ionization-time-of-flight"), of at least one reference sample, at least one given type of mammalian cells, said reference sample consisting of purified whole mammalian cells containing at least 95% of cells of said given type, and

2 / the extraction of the whole cells contained in the said biological sample to be analyzed is carried out, if necessary, in order to obtain an extract of whole cells, and 3 / the spectrum is produced by mass spectrometry of the following type;

MALDI-TOF of a said whole cell extract obtained in step 2 /, and

4 / the spectrum obtained in step 3 / is compared with:

4a / a reference spectrum of a purified extract of said given type of mammalian cells previously made in step 1 /, if one only seeks to identify whether said sample to be analyzed comprises a mammalian cell type of said given type mammalian cells, or

4b / a plurality of reference spectra made in step 1 / from a plurality of reference samples of a plurality of different given types of eukaryotic cells, said plurality of reference spectra comprising a spectral library of reference to different given types of mammalian cells, if one seeks to identify whether said sample to be analyzed comprises a mammalian cell type selected from different given types of mammalian cells. mammals whose spectra are included in said bank of reference spectra, and

5 / - a mammalian cell type of the extract obtained in step 2 /, from said sample to be analyzed, is identified in at least one of two ways:

5a / is identical to a given type of mammalian cells, if, in step 4 / above, is found in the spectrum obtained in step 3 / all the characteristic peaks of a said reference spectrum comprising at least 50 peaks, preferably at least 70 peaks,

5b / is as being close to at least one said given type of mammalian cells, by characterizing the proximity of the spectrum obtained in step 3 / with respect to at least one said reference spectrum of at least one said given type mammalian cells, by determining the number of characteristic peaks in common between the spectrum obtained and that of at least one said reference spectrum of at least one said type of mammalian cells.

2. Method according to claim 1, characterized in that, in step 2 / the separation is carried out of the different types of whole cells contained in said whole cell extract, to obtain at least one purified extract containing at least 95% d a type of whole cells.

3. Identification method according to claim 1 or 2, characterized in that a spectrum of a given type of cells is made from a cell extract having a cell concentration of at least 10 ⁵ cells / 1 μΙ said type of cells.

4. Identification method according to one of claims 1 to 3, characterized in that, in steps 1 / and 3 /, said spectra of reference samples and spectra of samples to be analyzed are taken into account only if said spectra have a quality score greater than 2, said quality score consisting of the addition of the two notes ni and n2 each having a value of 0 to 6 with:

- ni is the score given according to the number of characteristic peaks of said spectrum n, with. ni = 0 for n <8

. ni = 1 for 8 <n <27

. ni = 2 for 27 <n <43

. ni = 3 for 43 <n <78

. ni = 4 for 78 <n <86. ni = 5 for 86 <n <110

. ni = 6 for 110 <n, and

n2 is the score given according to the value of the signal-to-noise ratio (Rmax) of the peak of greater intensity of said spectrum, with

. n2 = 0 for Rmax <6.8. n2 = 1 for 6.8 <Rmax <26.8

. n2 = 2 for 26.8 <Rmax <52.6

. n2 = 3 for 52.6 <Rmax <208.6

. n2 = 4 for 208, 2 <Rmax <398.6

. n2 = 5 for 398.6 <Rmax≤l 092.2. n2 = 6 for 1 092.2 <Rmax.

5. Identification method according to one of claims 1 to 4, characterized in that the peaks of said spectra are defined by a pair of coordinates composed of an abscissa consisting of the ratio mass / charge (m / z) in the range of 2,000 to 15,000 Da, the ordinate coordinate being a relative intensity in arbitrary units, and retaining in the spectrum at least the 70 peaks of greater intensities.

6. Identification method according to one of claims 1 to

5, characterized in that it seeks to identify a given type of cells and is carried out of said reference spectra for at least one given type of cell selected from the following 22 types of eukaryotic cells: - human monocytes

- human T lymphocytes

- neutrophilic neutrophils

- human red blood cells

- human macrophages differentiated from monocytes - murine macrophages differentiated from bone marrow

- myelomonocytic human cell line THP-1

- immature macrophage murine cell line J774

- DH82 macrophage canine cell line

- differentiated monocyte dendritic cells - C8166 human cell line of CCR5 expressing lymphocytes

murine cell line of fibroblastic type L929

- HeLA epithelial human cell line

epithelial type human cell line 293T

- human cell line of trophoblastic type JEG BeWo trophoblastic type human cell line

placental cells of human trophoblasts

- XTC-2 cell line of Xenopus laevis

- amoebae cells Acanthamoeba polyphaga - Acanthamoeba amoebae cells caste / year //

- Hartmanella vermiformis amoebae cells

- amoebae cells Poteriochromonas melhamensis

7. Identification method according to claim 6, characterized in that the reference spectra of the 22 eukaryotic cell types comprise the characteristic peaks mentioned in the following Tables 1 to 22, in which the different characteristic peaks are characterized by their coordinates in FIG. abscissa m / z (Da) and their ordinates of relative intensity expressed as a percentage of the intensity of the peak of greater intensity (%), the values of the molecular masses m / z being able to vary up to ± 1 Da of the average value mentioned in said tables, and the relative intensity values that can vary up to ± 20% of the mean value of relative intensity mentioned in said tables, the spectra being made by analysis of whole cells in a matrix solution of 4-cyano-4-hydroxycinnamic acid:

Table 1: Spectrum Analysis of Circulating Human Monocytes

 Table 2: Analysis of the spectrum of circulating human T lymphocytes

Table 3: Spectrum Analysis of Human Polynuclear Neutrophils

Table 4: Ana lysis of the spectrum of human red blood cells

 Table 5: Analysis of the spectrum of human macrophages derived from monocytes

 Table 6: Dendritic cell spectrum analysis differentiated from human monocytes

 Table 7: Spectrum Analysis of Murine Macrophages Derived from Bone Marrow

Table 8. THPl cell spectrum analysis

Table 9: Spectrum Analysis of J774 Cells

 Table 10: Spectrum Analysis of DH82 Cells

 Table 11: Human T-cell Spectrum Analysis

C8166

 Table 12: Spectrum analysis of L929 cells

Table 13: HeLa Cell Spectrum Analysis

Table 14: 293T Cell Spectrum Analysis

Table 15: Spectrum Analysis of BeWo Cells

Table 16: JEG Cell Spectrum Analysis

 Table 17: Human Plasma Trophoblast Spectrum Analysis

Table 18: Spectrum Analysis of XTC-2 Cells

 Table 19: Cell spectrum analysis of Acanthamoeba polyphaga

 Table 20: Cell Spectrum Analysis of Acanthamoeba castellanii

 Table 21: Cell Spectrum Analysis & Hartmannella vermiformis

 Table 22: Cell Spectrum Analysis Poteriochromonas melhamensis

8. Identification method according to one of claims 1 to 7, characterized in that one identifies a given type of cells of mammals, preferably human, selected from the different types of circulating immune cells consisting of monocytes, T cells, neutrophils and red blood cells.

9. Identification method according to one of claims 1 to 7, characterized in that, in step 5 /, there is identified a given type of cells consisting of placental tissue trophoblast cells, preferably human.

10. Identification method according to one of claims 1 to

9, characterized in that, in step 5 /, the purification is monitored or the progress of the purification of a population of a given type of mammalian cells is monitored by comparing the spectrum obtained from of a cell type extract obtained during the purification process, with a said reference spectrum of said given type of mammalian cells.

11. Identification method according to one of claims 1 to

10, characterized in that:

in step 5 /, it is sought to identify a given activation state by a given activation factor of a given type of mammalian cells, and in step 4 /, one of the two steps 4a / and 4b / following, in which:

in step 4a /, a comparison is made with a reference spectrum of said given type of mammalian cells in a said activation state given by a given activation factor, or in step 4b /, said reference spectra database comprises spectra made from purified extracts of different given types of mammalian cells in different given activation states by different given activation factors.

12. Identification method according to one of claims 1 to

11, characterized in that, in step 5b /, an activation state of a given cell type is identified by characterizing its proximity with respect to different activation states by different activation factors, d. a given type of mammalian cells, by characterizing the proximity of the spectrum obtained in step 3 / with respect to different reference spectra respectively corresponding to the different activation states, by different activation factors, of said type given cells, by determining the number of peaks in common between the spectrum obtained and that of at least one said reference spectrum.

13. Identification method according to one of claims 11 or

12, characterized in that the different activation states given by different given activation factors of given types of mammalian cells are activation states of macrophages or circulating human cells, such as monocytes or lymphocytes, activated by a cytokine selected from IFN--, TGF--, TNF, IL-4, IL-10 or a bacterial ligand such as lipopolysaccharide (LPS) or peptidoglycan (PGN).

14. Identification method according to claim 13, characterized in that it is sought to identify a given activation state of a human macrophage with respect to activation states resulting from treatments for 18 hours with selected cytokines among IFN--, IL-4, TNF, IL-10, TGF-1, and bacterial ligands LPS and PGN, compared with reference spectra comprising the peaks mentioned in the following Tables 23 to 29, in which the different peaks are characterized by their coordinates in abscissa m / z (Da) and their coordinates in ordinates expressed in percentage of the intensity of the peak of greater intensity, the values of the molecular masses m / z being able to vary up to ± 1 Da of the average value mentioned in said tables, and the relative intensity values that can vary up to ± 20% of the mean value of relative intensity mentioned in said tables, the spectra being made by analysis of whole cells in a solution of α-cyano-4-hydroxycinnamic acid matrix:

Table 23: Spectrum of macrophages treated for 18 hours with 20 ng / ml of IFN- ^■

 Table 24 Spectrum of macrophages treated for 18 hours with 10 ng / ml of IL-4

 Table 25 Spectrum of macrophages treated for 18 hours with 10 ng / ml TNF

 Table 26. Spectrum of macrophages treated for 18 hours with 10 ng / ml of IL-10

m / z (Da) intensity m / z (Da) intensity m / z (Da) intensity

 Table 27: Spectrum of macrophages treated for 18 hours with 10 ng / ml of TGF-1

 Table 28: Spectrum of macrophages treated for 18 hours with 1 - g / ml of LPS

 Table 29: Spectrum of macrophages treated for 18 hours with 10 ng / ml PGN

 15. Identification method according to one of claims 1 to 14, characterized in that said whole mammalian cells that are analyzed to establish the mass spectrum, were frozen and thawed before establishing said mass spectrum.