9 research outputs found
Méthodes de résolution de courbes multivariées pour la microspectroscopie CARS
Visualization of biological samples such as cells or tissues is a common practice for biologists. This operation usually requires the addition of markers to highlight the constituents or molecules of interest. However, the addition of these markers requires several processing steps and alters the observed sample. To avoid these steps, an alternative allowing is vibrational microspectroscopy. This method allows to use the vibration of chemical bonds to acquire a spectrum characterizing the chemical composition of the sample. The acquisition of several points allows to acquire a cartography with a strong spectral richness. In order to exploit this richness and characterize the composition of the specimen studied, the multivariate curve resolution (MCR) aims to determine the components present by characterizing their spectral signature and their concentration at each measurement point. Nowadays, the MCR is essentially solved by linear regressions and does not take into account the spatial aspect of the data. In this thesis, the application of multivariate curve resolution to cell and tissue acquisitions with coherent anti-Stokes Raman scattering is introduced. In a second step, a segmentation constraint is integrated into the MCR by implementing an active contour segmentation. Finally, the use of autoencoders to accomplish the MCR and integrate spatial information is studied. The results allowed to highlight the visualization of different organelles present within cells in agreement with the state of the art as well as a characterization of their spectral signature. The addition of the constraint allows an efïŹcient segmentation of cells and, com- bined with the results without segmentation, brings additional information for the analysis of the results. The study of autoencoders highlights their potential to apply the MCR while addressing the limitations related to the random initialization of the network weights.La visualisation dâĂ©chantillons biologiques comme les cellules ou les tissus est une pratique habituelle pour les biologistes. Cette opĂ©ration nĂ©cessite le plus souvent lâajout de marqueurs pour mettre en Ă©vidence les constituants ou molĂ©cules dâintĂ©rĂȘts. Cependant, lâajout de ces marqueurs nĂ©cessite plusieurs Ă©tapes de traitement et altĂšre de maniĂšre irrĂ©mĂ©diable lâĂ©chantillon observĂ©. Une alternative permettant de sâabstraire des marqueurs est la microspectroscopie vibrationnelle. Cette mĂ©thode permet dâutiliser le phĂ©nomĂšne de vibration des liaisons chimiques pour acquĂ©rir un spectre caractĂ©risant la composition chimique de lâĂ©chantillon. Lâutilisation de cette mĂ©thode en plusieurs points permet dâacquĂ©rir une cartographie avec une forte richesse spectrale. AïŹn dâexploiter cette richesse et caractĂ©riser au mieux la composition du spĂ©cimen Ă©tudiĂ©, la rĂ©solution de courbes multivariĂ©es (ou MCR de lâanglais multivariate curve resolution) a pour objectif de dĂ©terminer les composants prĂ©sents en caractĂ©risant leur signature spectrale et leur concentration en chaque point de mesure de la cartographie. De nos jours, la MCR est essentiellement rĂ©solue par des rĂ©gressions linĂ©aires et ne tient pas compte de lâaspect spatial des donnĂ©es. Dans cette thĂšse, lâapplication de la rĂ©solution de courbes multivariĂ©es Ă des acquisitions de cellules et tissus utilisant la mĂ©thode de diffusion Raman anti-Stokes cohĂ©rente est introduite. Dans un second temps, une contrainte de segmentation est intĂ©grĂ©e au sein de la MCR par lâimplĂ©mentation dâune segmentation par contour actif. Pour ïŹnir, lâutilisation dâauto-encodeurs pour accomplir la MCR et intĂ©grĂ© lâinformation spatiale est Ă©tudiĂ©e. Les rĂ©sultats obtenus ont permis de mettre en Ă©vidence la visualisation de diffĂ©rents organites prĂ©sents au sein de cellules en accord avec lâĂ©tat de lâart ainsi quâune caractĂ©risation de leur signature spectrale. Lâajout de la contrainte permet une segmentation efïŹcace de cellules et, combinĂ© avec les rĂ©sultats sans segmentation, apporte une information supplĂ©mentaire pour lâanalyse des rĂ©sultats. LâĂ©tude des auto-encodeurs met en Ă©vidence leur potentiel pour appliquer la MCR tout en abordant les limites liĂ©es Ă lâinitialisation alĂ©atoire des poids du rĂ©seau
Multivariate curve resolution methods for CARS microspectroscopy
La visualisation dâĂ©chantillons biologiques comme les cellules ou les tissus est une pratique habituelle pour les biologistes. Cette opĂ©ration nĂ©cessite le plus souvent lâajout de marqueurs pour mettre en Ă©vidence les constituants ou molĂ©cules dâintĂ©rĂȘts. Cependant, lâajout de ces marqueurs nĂ©cessite plusieurs Ă©tapes de traitement et altĂšre de maniĂšre irrĂ©mĂ©diable lâĂ©chantillon observĂ©. Une alternative permettant de sâabstraire des marqueurs est la microspectroscopie vibrationnelle. Cette mĂ©thode permet dâutiliser le phĂ©nomĂšne de vibration des liaisons chimiques pour acquĂ©rir un spectre caractĂ©risant la composition chimique de lâĂ©chantillon. Lâutilisation de cette mĂ©thode en plusieurs points permet dâacquĂ©rir une cartographie avec une forte richesse spectrale. AïŹn dâexploiter cette richesse et caractĂ©riser au mieux la composition du spĂ©cimen Ă©tudiĂ©, la rĂ©solution de courbes multivariĂ©es (ou MCR de lâanglais multivariate curve resolution) a pour objectif de dĂ©terminer les composants prĂ©sents en caractĂ©risant leur signature spectrale et leur concentration en chaque point de mesure de la cartographie. De nos jours, la MCR est essentiellement rĂ©solue par des rĂ©gressions linĂ©aires et ne tient pas compte de lâaspect spatial des donnĂ©es. Dans cette thĂšse, lâapplication de la rĂ©solution de courbes multivariĂ©es Ă des acquisitions de cellules et tissus utilisant la mĂ©thode de diffusion Raman anti-Stokes cohĂ©rente est introduite. Dans un second temps, une contrainte de segmentation est intĂ©grĂ©e au sein de la MCR par lâimplĂ©mentation dâune segmentation par contour actif. Pour ïŹnir, lâutilisation dâauto-encodeurs pour accomplir la MCR et intĂ©grĂ© lâinformation spatiale est Ă©tudiĂ©e. Les rĂ©sultats obtenus ont permis de mettre en Ă©vidence la visualisation de diffĂ©rents organites prĂ©sents au sein de cellules en accord avec lâĂ©tat de lâart ainsi quâune caractĂ©risation de leur signature spectrale. Lâajout de la contrainte permet une segmentation efïŹcace de cellules et, combinĂ© avec les rĂ©sultats sans segmentation, apporte une information supplĂ©mentaire pour lâanalyse des rĂ©sultats. LâĂ©tude des auto-encodeurs met en Ă©vidence leur potentiel pour appliquer la MCR tout en abordant les limites liĂ©es Ă lâinitialisation alĂ©atoire des poids du rĂ©seau.Visualization of biological samples such as cells or tissues is a common practice for biologists. This operation usually requires the addition of markers to highlight the constituents or molecules of interest. However, the addition of these markers requires several processing steps and alters the observed sample. To avoid these steps, an alternative allowing is vibrational microspectroscopy. This method allows to use the vibration of chemical bonds to acquire a spectrum characterizing the chemical composition of the sample. The acquisition of several points allows to acquire a cartography with a strong spectral richness. In order to exploit this richness and characterize the composition of the specimen studied, the multivariate curve resolution (MCR) aims to determine the components present by characterizing their spectral signature and their concentration at each measurement point. Nowadays, the MCR is essentially solved by linear regressions and does not take into account the spatial aspect of the data. In this thesis, the application of multivariate curve resolution to cell and tissue acquisitions with coherent anti-Stokes Raman scattering is introduced. In a second step, a segmentation constraint is integrated into the MCR by implementing an active contour segmentation. Finally, the use of autoencoders to accomplish the MCR and integrate spatial information is studied. The results allowed to highlight the visualization of different organelles present within cells in agreement with the state of the art as well as a characterization of their spectral signature. The addition of the constraint allows an efïŹcient segmentation of cells and, com- bined with the results without segmentation, brings additional information for the analysis of the results. The study of autoencoders highlights their potential to apply the MCR while addressing the limitations related to the random initialization of the network weights
Méthodes de résolution de courbes multivariées pour la microspectroscopie CARS
Visualization of biological samples such as cells or tissues is a common practice for biologists. This operation usually requires the addition of markers to highlight the constituents or molecules of interest. However, the addition of these markers requires several processing steps and alters the observed sample. To avoid these steps, an alternative allowing is vibrational microspectroscopy. This method allows to use the vibration of chemical bonds to acquire a spectrum characterizing the chemical composition of the sample. The acquisition of several points allows to acquire a cartography with a strong spectral richness. In order to exploit this richness and characterize the composition of the specimen studied, the multivariate curve resolution (MCR) aims to determine the components present by characterizing their spectral signature and their concentration at each measurement point. Nowadays, the MCR is essentially solved by linear regressions and does not take into account the spatial aspect of the data. In this thesis, the application of multivariate curve resolution to cell and tissue acquisitions with coherent anti-Stokes Raman scattering is introduced. In a second step, a segmentation constraint is integrated into the MCR by implementing an active contour segmentation. Finally, the use of autoencoders to accomplish the MCR and integrate spatial information is studied. The results allowed to highlight the visualization of different organelles present within cells in agreement with the state of the art as well as a characterization of their spectral signature. The addition of the constraint allows an efïŹcient segmentation of cells and, com- bined with the results without segmentation, brings additional information for the analysis of the results. The study of autoencoders highlights their potential to apply the MCR while addressing the limitations related to the random initialization of the network weights.La visualisation dâĂ©chantillons biologiques comme les cellules ou les tissus est une pratique habituelle pour les biologistes. Cette opĂ©ration nĂ©cessite le plus souvent lâajout de marqueurs pour mettre en Ă©vidence les constituants ou molĂ©cules dâintĂ©rĂȘts. Cependant, lâajout de ces marqueurs nĂ©cessite plusieurs Ă©tapes de traitement et altĂšre de maniĂšre irrĂ©mĂ©diable lâĂ©chantillon observĂ©. Une alternative permettant de sâabstraire des marqueurs est la microspectroscopie vibrationnelle. Cette mĂ©thode permet dâutiliser le phĂ©nomĂšne de vibration des liaisons chimiques pour acquĂ©rir un spectre caractĂ©risant la composition chimique de lâĂ©chantillon. Lâutilisation de cette mĂ©thode en plusieurs points permet dâacquĂ©rir une cartographie avec une forte richesse spectrale. AïŹn dâexploiter cette richesse et caractĂ©riser au mieux la composition du spĂ©cimen Ă©tudiĂ©, la rĂ©solution de courbes multivariĂ©es (ou MCR de lâanglais multivariate curve resolution) a pour objectif de dĂ©terminer les composants prĂ©sents en caractĂ©risant leur signature spectrale et leur concentration en chaque point de mesure de la cartographie. De nos jours, la MCR est essentiellement rĂ©solue par des rĂ©gressions linĂ©aires et ne tient pas compte de lâaspect spatial des donnĂ©es. Dans cette thĂšse, lâapplication de la rĂ©solution de courbes multivariĂ©es Ă des acquisitions de cellules et tissus utilisant la mĂ©thode de diffusion Raman anti-Stokes cohĂ©rente est introduite. Dans un second temps, une contrainte de segmentation est intĂ©grĂ©e au sein de la MCR par lâimplĂ©mentation dâune segmentation par contour actif. Pour ïŹnir, lâutilisation dâauto-encodeurs pour accomplir la MCR et intĂ©grĂ© lâinformation spatiale est Ă©tudiĂ©e. Les rĂ©sultats obtenus ont permis de mettre en Ă©vidence la visualisation de diffĂ©rents organites prĂ©sents au sein de cellules en accord avec lâĂ©tat de lâart ainsi quâune caractĂ©risation de leur signature spectrale. Lâajout de la contrainte permet une segmentation efïŹcace de cellules et, combinĂ© avec les rĂ©sultats sans segmentation, apporte une information supplĂ©mentaire pour lâanalyse des rĂ©sultats. LâĂ©tude des auto-encodeurs met en Ă©vidence leur potentiel pour appliquer la MCR tout en abordant les limites liĂ©es Ă lâinitialisation alĂ©atoire des poids du rĂ©seau
Multivariate curve resolution with autoencoders for CARS microspectroscopy
International audienceCoherent anti-Stokes Raman scattering (CARS) microspectroscopy is a powerful tool for label-free cell imaging thanks to its ability to acquire a rich amount of information. An important family of operations applied to such data is multivariate curve resolution (MCR). It aims to find main components of a dataset and compute their spectra and concentrations in each pixel. Recently, autoencoders began to be studied to accomplish MCR with dense and convolutional models. However, many questions, like the results variability or the reconstruction metric, remain open and applications are limited to hyperspectral imaging. In this article, we present a nonlinear convolutional encoder combined with a linear decoder to apply MCR to CARS microspectroscopy. We conclude with a study of the result variability induced by the encoder initialization
Segmentation integration in multivariate curve resolution applied to coherent anti-Stokes Raman scattering
International audienc
CARSData: Data analysis and machine learning applied to CARS spectroscopyâ. 3rd edition of the ÎŁ-Tech Days
International audienc
Recent advances in cell and tissue imaging by multiplex CARS microspectroscopy
International audienc
Toward Whole Brain Label-free Molecular Imaging with Single-cell Resolution sing Ultra-broadband Multiplex CARS Microspectroscopy
International audienceMapping the distribution of chemical molecules throughout a brain is helpful for neuroscience research. We have applied an ultra-broadband multiplex CARS spectroscopic imaging system to construct a whole-brain label-free molecular map in macro and micro scales. We could precisely visualize lipids distributed to white matter, rich in neuronal fibers. Our microscale measurements figured out that cells within the hippocampus and cerebral cortex could be divided into lipid-rich and water-rich cells. Moreover, we applied multivariate curve decomposition analysis for our spectrum and recapitulated the results. Our imaging and analysis techniques will lead to the molecular brain atlas with single-cell resolution
Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis
International audienceCoherent Raman imaging has been extensively applied to live-cell imaging in the last two decades, allowing to probe the intracellular lipid, protein, nucleic acid and water content with high acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here we introduce the combination of MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and, as well, by considering cell clusters of various sizes