Search

Single-cell spatially resolved proteomic or transcriptomic methods offer the opportunity to discover cell types interactions of biological or clinical importance. To extract relevant information from these data, we presentmosna, a Python package to analyze spatially resolved experiments and discover patterns of cellular spatial organization. It includes the detection of preferential interactions between specific cell types and the discovery of cellular niches. We exemplify the proposed analysis pipeline on spatially resolved proteomic data from cancer patient samples annotated with clinical response to immunotherapy, and we show thatmosnacan identify a number of features describing cellular composition and spatial distribution that can provide biological hypotheses regarding factors that affect response to therapies.

Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures, either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based on DNA methylation or RNAseq data, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, bone marrow from multiple myeloma patients and a single-cell melanoma dataset. Cell proportions estimates based on deconvolution performed using our signature matrices, implemented within the EpiDISH framework, show comparable or better correlation with FACS measurements of immune cell-type abundance and with various estimates of cancer sample purity and composition than existing methods. Using publicly available data of 3D chromatin structure in haematopoietic cells, we expanded the list of genes to be included in the RNAseq signature matrices by considering the presence of methylated CpGs in gene promoters or in genomic regions which are in 3D contact with these promoters. Our expanded signature matrices have improved performance compared to our initial RNAseq signature matrix. Finally, we show the value of our signatures in predicting patient response to immune checkpoint inhibitors in three melanoma and one bladder cancer cohorts, based on bulk tumour sample gene expression. We also provide GEM-DeCan: a snakemake pipeline, able to run an analysis from raw sequencing data to deconvolution based on various gene expression signature matrices, both for bulk RNASeq and DNA methylation data. The code for producing the signature matrices and reproducing all the figures of this paper is available on the GEM-DeCan repository. Keywords: Deconvolution, Tumour microenvironment, DNA methylation, gene expression, EpiDISH, deconRNAseq, quanTIseq, MCP-Counter, Promoter Capture Hi-C, chromatin network

We wish to acknowledge the following funding bodies: National Health of Medical Research Council Career Development Fellowship AU, (KALC); Chan Zuckerberg initiative (AJA, VJC, ACC); Australian Research Council (AJA, KALC, MER); National Institute of Health, (EFDM, AD, SH); National Cancer Institute, (EFDM, VJC, CSG, GCY, EJF); Chair of bioinformatics in oncology of the CRCT (INSERM, Fondation Toulouse Cancer Santé, Pierre Fabre Research Institute, (AC, VP); Fondation Toulouse Cancer Santé, (AC, VP); Pierre Fabre Research Institute, (AC, VP); Michael Smith Foundation for Health Research, (AS); Mitacs, (AS); Kavli Neuroscience Discovery Institute, (GLSO); Johns Hopkins Provost Postdoctoral Fellowship, (GLSO); National Human Genome Research Institute, NIH, (JDW, VJC, CSG, WM, MIL); National Institute of Allergy and Infectious Diseases, NIH, (JDW, SH); National Institute of Mental Health, NIH, (JDW, MIL); EMBL PhD program, (RA); National Institutes of Health, National Institute on Aging, (GCY); DoD, (ACC); National Institute of Health, National Institute of Dental and Craniofacial Research, (EJF); Lustgarten Foundation, (EJF); Emerson Foundation, (EJF); Allegheny Health Network, (EJF). Peer Reviewed "Article signat per 31 autors/es: Kim-Anh Lê Cao, Al J. Abadi, Emily F. Davis-Marcisak, Lauren Hsu, Arshi Arora, Alexis Coullomb, Atul Deshpande, Yuzhou Feng, Pratheepa Jeganathan, Melanie Loth, Chen Meng, Wancen Mu, Vera Pancaldi, Kris Sankaran, Dario Righelli, Amrit Singh, Joshua S. Sodicoff, Genevieve L. Stein-O’Brien, Ayshwarya Subramanian, Joshua D. Welch, Yue You, Ricard Argelaguet, Vincent J. Carey, Ruben Dries, Casey S. Greene, Susan Holmes, Michael I. Love, Matthew E. Ritchie, Guo-Cheng Yuan, Aedin C. Culhane & Elana Fertig "

By opening space for public discussion where people can feel heard and respected, democratic societies can not only achieve better results, but also restore a level of trust in institutions and a sense of belonging to communities that are dangerously crumbling. A Viewpoint from the Spring 2020 issue., Stanford Social Innovation Review, 18(2), 61-62

Foerster Resonance Energy Transfer (FRET) allows for the visualization of nanometer-scale distances and distance changes. This sensitivity is regularly achieved in single-molecule experiments in vitro but is still challenging in biological materials. Despite many efforts, quantitative FRET in living samples is either restricted to specific instruments or limited by the complexity of the required analysis. With the recent development and expanding utilization of FRET-based biosensors, it becomes essential to allow biologists to produce quantitative results that can directly be compared. Here, we present a new calibration and analysis method allowing for quantitative FRET imaging in living cells with a simple fluorescence microscope. Aside from the spectral crosstalk corrections, two additional correction factors were defined from photophysical equations, describing the relative differences in excitation and detection efficiencies. The calibration is achieved in a single step, which renders the Quantitative Three-Image FRET (QuanTI-FRET) method extremely robust. The only requirement is a sample of known stoichiometry donor:acceptor, which is naturally the case for intramolecular FRET constructs. We show that QuanTI-FRET gives absolute FRET values, independent of the instrument or the expression level. Through the calculation of the stoichiometry, we assess the quality of the data thus making QuanTI-FRET usable confidently by non-specialists., 13 pages, 3 figures

Living cells can react to mechanical signals such as the rigidity of the surface they adhere on, the traction or compression forces applied on them, the liquid flow at their membrane surface or the geometry of their adhesions or of their overall shape. Those signals influence cellular processes such as proliferation, differentiation, migration or cell death. Those processes are tightly regulated by biochemical reactions that constitute a signaling network. Mechanotransduction is the translation of the mechanical signal into the biochemical one.In order to study mechanotransduction, we have considered the use of ultrasounds to mechanically stimulate cells at relatively high temporal and spatial frequencies. Numerous setups and options have been considered in this very exploratory project. Finally, we will retain some promising leads for the continuation of this project.We have developed what we call active substrates that allows us to control both spatially and temporally the mechanical stimulation on living cells. Those active substrates consist of iron micropillars embedded in a soft elastomer and actuated by 2 electromagnets. We can control dynamically the displacement of the pillar that will deform locally and continuously the surface. This deformation will then deform in traction or in compression the living cells spread on the surface nearby. Thanks to fluorescent trackers we can perform Traction Force Microscopy and monitor the stress applied by the pillars to the cells through the PDMS surface, and we can look at the mechanical response of the cells. Moreover, those substrates are compatible with live cell fluorescence microscopy, which makes possible the observation of the cellular response at the morphological level (focal adhesions, protrusive activity, …) and most importantly at the biochemical level.Indeed, in order to study the cellular biochemical response after a mechanical stimulation, we use fluorescence microscopy to observe biosensors containing pairs of donor/acceptor fluorophores. Those biosensors allow us to monitor the activity of proteins implied in cellular signaling by computing the Förster Resonance Energy Transfer (FRET) efficiency of those biosensors. To do so, samples are alternatively excited at donor and acceptor excitation wavelengths. The fluorescence signal is then simultaneously measured in donor and acceptor emission channels. A substantial part of my thesis has been dedicated to the development of a quantitative method to analyze fluorescence images in order to measure FRET efficiencies that do not depend on experimental factors or biosensors concentration in cells. We assess different methods to compute standard correction factors that account for spectral bleed-through and direct excitation of acceptors at donor excitation wavelength. To obtain more quantitative measurements, we have developed a new method to compute 2 additional correction factors. We compare this method with the only one preexisting, and we assess the influence of image processing parameters on FRET efficiency values.; Les cellules vivantes sont capables de réagir aux signaux mécaniques tels que la rigidité de la surface sur laquelle elles adhèrent, les forces de tractions ou compressions auxquelles elles sont soumises, le flux de liquide à la surface de leur membrane ou encore la géométrie de leurs adhésions ou de leur forme globale. Ces signaux influent sur des processus cellulaires tels que la prolifération, la différenciation, la migration et la mort cellulaire. Ces processus sont finement régulés par des réactions biochimiques qui forment un réseau de signalisation. La mécanotransduction est la traduction du signal mécanique en signal biochimique.C’est dans le but d’étudier la mécanotransduction que nous avons étudié l’utilisation d’ultrasons pour stimuler mécaniquement les cellules à des fréquences temporelles et spatiales relativement élevées. De nombreux montages expérimentaux et de nombreuses voies ont été considérées dans cette partie très exploratoire. Nous en retenons finalement des pistes prometteuses pour la continuation future de ce projet.Nous avons développé ce que nous nommons des substrats actifs, qui nous permettent de contrôler à la fois spatialement et temporellement la stimulation mécanique appliquée à des cellules vivantes. Ces substrats actifs consistent en des micropiliers de fer incrustés dans un élastomère peu rigide (PDMS) et manipulés par deux électroaimants. Nous pouvons contrôler dynamiquement le déplacement des piliers qui vont déformer localement et de manière continue la surface. Cette déformation va ensuite déformer en traction ou en compression les cellules vivantes étalées sur la surface à proximité. En employant des marqueurs fluorescents nous pouvons réaliser de la Microscopie de Forces de Traction et surveiller la contrainte appliquée par les piliers aux cellules à travers la surface de PDMS, et nous pouvons étudier la réponse mécanique des cellules. De plus, ces substrats sont compatibles avec la microscopie de fluorescence en cellule vivante, ce qui rend possible l’observation de la réponse cellulaire au niveau morphologique (forme des adhésions focales, activité protrusive, …) et surtout biochimique.En effet, pour étudier la réponse biochimique des cellules après une stimulation mécanique, nous observons par microscopie de fluorescence des biosenseurs portant des paires de fluorophores donneur/accepteur. Ces biosenseurs nous permettent d’observer l’activité de protéines impliquées dans la signalisation cellulaire en calculant l’efficacité de Transfert d’Énergie Résonnant de Förster (FRET) de ces biosenseurs. Pour ce faire, les échantillons sont illuminés alternativement aux longueurs d’ondes d’excitation des fluorophores donneurs puis accepteurs. Le signal de fluorescence est collecté simultanément dans un canal d’émission du donneur et un canal d’émission de l’accepteur. Une grande partie de ma thèse a été consacrée à la mise au point d’une méthode quantitative pour analyser les images de fluorescence afin de mesurer une efficacité de FRET qui ne dépende pas de facteurs expérimentaux ni de la quantité de biosenseurs présents dans les cellules. Nous évaluons alors les différentes méthodes pour déterminer les facteurs de correction répandus corrigeant le débordement de spectre du donneur dans le canal accepteur et l’excitation directe de l’accepteur à la longueur d’onde d’excitation du donneur. Pour obtenir des mesures plus quantitatives, nous avons mis au point une nouvelles méthode pour déterminer 2 facteurs de correction supplémentaires. Nous comparons cette méthode à la seule préexistante et évaluons l’influence des paramètres de traitement des images sur les valeurs d’efficacité de FRET mesurées.

Living cells can react to mechanical signals such as the rigidity of the surface they adhere on, the traction or compression forces applied on them, the liquid flow at their membrane surface or the geometry of their adhesions or of their overall shape. Those signals influence cellular processes such as proliferation, differentiation, migration or cell death. Those processes are tightly regulated by biochemical reactions that constitute a signaling network. Mechanotransduction is the translation of the mechanical signal into the biochemical one.In order to study mechanotransduction, we have considered the use of ultrasounds to mechanically stimulate cells at relatively high temporal and spatial frequencies. Numerous setups and options have been considered in this very exploratory project. Finally, we will retain some promising leads for the continuation of this project.We have developed what we call active substrates that allows us to control both spatially and temporally the mechanical stimulation on living cells. Those active substrates consist of iron micropillars embedded in a soft elastomer and actuated by 2 electromagnets. We can control dynamically the displacement of the pillar that will deform locally and continuously the surface. This deformation will then deform in traction or in compression the living cells spread on the surface nearby. Thanks to fluorescent trackers we can perform Traction Force Microscopy and monitor the stress applied by the pillars to the cells through the PDMS surface, and we can look at the mechanical response of the cells. Moreover, those substrates are compatible with live cell fluorescence microscopy, which makes possible the observation of the cellular response at the morphological level (focal adhesions, protrusive activity, …) and most importantly at the biochemical level.Indeed, in order to study the cellular biochemical response after a mechanical stimulation, we use fluorescence microscopy to observe biosensors containing pairs of donor/acceptor fluorophores. Those biosensors allow us to monitor the activity of proteins implied in cellular signaling by computing the Förster Resonance Energy Transfer (FRET) efficiency of those biosensors. To do so, samples are alternatively excited at donor and acceptor excitation wavelengths. The fluorescence signal is then simultaneously measured in donor and acceptor emission channels. A substantial part of my thesis has been dedicated to the development of a quantitative method to analyze fluorescence images in order to measure FRET efficiencies that do not depend on experimental factors or biosensors concentration in cells. We assess different methods to compute standard correction factors that account for spectral bleed-through and direct excitation of acceptors at donor excitation wavelength. To obtain more quantitative measurements, we have developed a new method to compute 2 additional correction factors. We compare this method with the only one preexisting, and we assess the influence of image processing parameters on FRET efficiency values., Les cellules vivantes sont capables de réagir aux signaux mécaniques tels que la rigidité de la surface sur laquelle elles adhèrent, les forces de tractions ou compressions auxquelles elles sont soumises, le flux de liquide à la surface de leur membrane ou encore la géométrie de leurs adhésions ou de leur forme globale. Ces signaux influent sur des processus cellulaires tels que la prolifération, la différenciation, la migration et la mort cellulaire. Ces processus sont finement régulés par des réactions biochimiques qui forment un réseau de signalisation. La mécanotransduction est la traduction du signal mécanique en signal biochimique.C’est dans le but d’étudier la mécanotransduction que nous avons étudié l’utilisation d’ultrasons pour stimuler mécaniquement les cellules à des fréquences temporelles et spatiales relativement élevées. De nombreux montages expérimentaux et de nombreuses voies ont été considérées dans cette partie très exploratoire. Nous en retenons finalement des pistes prometteuses pour la continuation future de ce projet.Nous avons développé ce que nous nommons des substrats actifs, qui nous permettent de contrôler à la fois spatialement et temporellement la stimulation mécanique appliquée à des cellules vivantes. Ces substrats actifs consistent en des micropiliers de fer incrustés dans un élastomère peu rigide (PDMS) et manipulés par deux électroaimants. Nous pouvons contrôler dynamiquement le déplacement des piliers qui vont déformer localement et de manière continue la surface. Cette déformation va ensuite déformer en traction ou en compression les cellules vivantes étalées sur la surface à proximité. En employant des marqueurs fluorescents nous pouvons réaliser de la Microscopie de Forces de Traction et surveiller la contrainte appliquée par les piliers aux cellules à travers la surface de PDMS, et nous pouvons étudier la réponse mécanique des cellules. De plus, ces substrats sont compatibles avec la microscopie de fluorescence en cellule vivante, ce qui rend possible l’observation de la réponse cellulaire au niveau morphologique (forme des adhésions focales, activité protrusive, …) et surtout biochimique.En effet, pour étudier la réponse biochimique des cellules après une stimulation mécanique, nous observons par microscopie de fluorescence des biosenseurs portant des paires de fluorophores donneur/accepteur. Ces biosenseurs nous permettent d’observer l’activité de protéines impliquées dans la signalisation cellulaire en calculant l’efficacité de Transfert d’Énergie Résonnant de Förster (FRET) de ces biosenseurs. Pour ce faire, les échantillons sont illuminés alternativement aux longueurs d’ondes d’excitation des fluorophores donneurs puis accepteurs. Le signal de fluorescence est collecté simultanément dans un canal d’émission du donneur et un canal d’émission de l’accepteur. Une grande partie de ma thèse a été consacrée à la mise au point d’une méthode quantitative pour analyser les images de fluorescence afin de mesurer une efficacité de FRET qui ne dépende pas de facteurs expérimentaux ni de la quantité de biosenseurs présents dans les cellules. Nous évaluons alors les différentes méthodes pour déterminer les facteurs de correction répandus corrigeant le débordement de spectre du donneur dans le canal accepteur et l’excitation directe de l’accepteur à la longueur d’onde d’excitation du donneur. Pour obtenir des mesures plus quantitatives, nous avons mis au point une nouvelles méthode pour déterminer 2 facteurs de correction supplémentaires. Nous comparons cette méthode à la seule préexistante et évaluons l’influence des paramètres de traitement des images sur les valeurs d’efficacité de FRET mesurées.

Cells are able to sense and react to their physical environment by translating a mechanical cue into an intracellular biochemical signal that triggers biological and mechanical responses. This process, called mechanotransduction, controls essential cellular functions such as proliferation and migration. The cellular response to an external mechanical stimulation has been investigated with various static and dynamic systems, so far limited to global deformations or to local stimulation through discrete substrates. To apply local and dynamic mechanical constraints at the single cell scale through a continuous surface, we have developed and modelled magneto-active substrates made of magnetic micro-pillars embedded in an elastomer. Constrained and unconstrained substrates are analysed to map surface stress resulting from the magnetic actuation of the micro-pillars and the adherent cells. These substrates have a rigidity in the range of cell matrices, and the magnetic micro-pillars generate local forces in the range of cellular forces, both in traction and compression. As an application, we followed the protrusive activity of cells subjected to dynamic stimulations. Our magneto-active substrates thus represent a new tool to study mechanotransduction in single cells, and complement existing techniques by exerting a local and dynamic stimulation, traction and compression, through a continuous soft substrate.

International audience; Mixed plutonium and uranium monocarbide (UPuC) is considered as a possible fuel material for future nuclear gas fast reactors. Its safe handling is currently a major concern, because inflammation of this material under the shape of fine powders is easy and highly exothermic (pyrophoricity) even under ambient temperature and partial pressure of oxygen inferior to 0.2 bar. CEA Marcoule is implied in both experimental and numerical studies on the UC powder oxidation exothermic reaction. Experimental tests consist in determining the influence of various parameters (gas composition, heating ramp, specific surface of powders) on the sample inflammation temperature. Two kinds of analytical apparatus are used: The differential thermal analysis (DTA) and the differential scanning calorimetry (DSC) coupled to the thermo gravimetric analysis (TGA). These apparatus are also linked to a gas mass spectrometer to follow the composition of combustion chamber gases. Results obtained with small quantities revealed that UC powder is highly reactive in air in the temperature range of 150-250 degrees C and showed a strong dependence between powder height in crucibles and inflammation temperature.

International audience; Mixed plutonium and uranium carbide (UPuC) is considered as a possible fuel material for future nuclear reactors. However, UPuC is pyrophoric and fine powders of UPuC are subject to temperature increase due to oxidation with air and possible ignition during conditioning and handling. In a first approach and to allow easier experimental conditions, this study was undertaken on uranium monocarbide (UC) with the aim to determine safe handling conditions for the production and reprocessing of uranium carbide fuels. The reactivity of uranium monocarbide in oxidizing atmosphere was studied in order to analyze the ignition process. Experimental thermogravimetric analysis (TGA) and differential thermal analysis (DTA) revealed that UC powder obtained by arc melting and milling is highly reactive in air at about 200 degrees C. The phases formed at the various observed stages of the oxidation process were analyzed by Xray diffraction. At the same time, ignition was analyzed thermodynamically along isothermal sections of the U-C-O ternary diagram and the pressure of the gas produced by the UC + O-2 reaction was calculated. Two possible oxidation schemes were identified on the U-C-O phase diagram and assumptions are proposed concerning the overall oxidation and ignition paths. It is particularly important to understand the mechanisms involved since temperatures as high as 2500 degrees C could be reached, leading to CO(g) production and possibly to a blast effect. (C) 2009 Elsevier B.V. All rights reserved.