Death and the Societies of Late Antiquity

Ce volume bilingue, comprenant un ensemble de 28 contributions disponibles en français et en anglais (dans leur version longue ou abrégée), propose d’établir un état des lieux des réflexions, recherches et études conduites sur le fait funéraire à l’époque tardo-antique au sein des provinces de l’Empire romain et sur leurs régions limitrophes, afin d’ouvrir de nouvelles perspectives sur ses évolutions possibles. Au cours des trois dernières décennies, les transformations considérables des méthodologies déployées sur le terrain et en laboratoire ont permis un renouveau des questionnements sur les populations et les pratiques funéraires de l’Antiquité tardive, période marquée par de multiples changements politiques, sociaux, démographiques et culturels. L’apparition de ce qui a été initialement désigné comme une « Anthropologie de terrain », qui fut le début de la démarche archéothanatologique, puis le récent développement d’approches collaboratives entre des domaines scientifiques divers (archéothanatologie, biochimie et géochimie, génétique, histoire, épigraphie par exemple) ont été décisives pour le renouvellement des problématiques d’étude : révision d’anciens concepts comme apparition d’axes d’analyse inédits. Les recherches rassemblées dans cet ouvrage sont articulées autour de quatre grands thèmes : l’évolution des pratiques funéraires dans le temps, l’identité sociale dans la mort, les ensembles funéraires en transformation (organisation et topographie) et les territoires de l’empire (du cœur aux marges). Ces études proposent un réexamen et une révision des données, tant anthropologiques qu’archéologiques ou historiques sur l’Antiquité tardive, et révèlent, à cet égard, une mosaïque de paysages politiques, sociaux et culturels singulièrement riches et complexes. Elles accroissent nos connaissances sur le traitement des défunts, l’emplacement des aires funéraires ou encore la structure des sépultures, en révélant une diversité de pratiques, et permettent au final de relancer la réflexion sur la manière dont les sociétés tardo-antiques envisagent la mort et sur les éléments permettant d’identifier et de définir la diversité des groupes qui les composent. Elles démontrent ce faisant que nous pouvons véritablement appréhender les structures culturelles et sociales des communautés anciennes et leurs potentielles transformations, à partir de l’étude des pratiques funéraires.This bilingual volume proposes to draw up an assessment of the recent research conducted on funerary behavior during Late Antiquity in the provinces of the Roman Empire and on their borders, in order to open new perspectives on its possible developments. The considerable transformations of the methodologies have raised the need for a renewal of the questions on the funerary practices during Late Antiquity, a period marked by multiple political, social, demographic and cultural changes. The emergence field anthropology, which was the beginning of archaeothanatology, and then the recent development of collaborative approaches between various scientific fields (archaeothanatology, biochemistry and geochemistry, genetics, history, epigraphy, for example), have been decisive. The research collected in this book is structured around four main themes: Evolution of funerary practices over time; Social identity through death; Changing burial grounds (organisation and topography); Territories of the Empire (from the heart to the margins). These studies propose a review and a revision of the data, both anthropological and archaeological or historical on Late Antiquity, and reveal a mosaic of political, social, and cultural landscapes singularly rich and complex. In doing so, they demonstrate that we can truly understand the cultural and social structures of ancient communities and their potential transformations, based on the study of funerary practices

Study of metabolic and epigenetic dynamics during hematopoietic stem cells differentiation

Mes travaux de thèse explorent l’articulation entre le métabolisme, l’état chromatinien et la transcription au cours des premières étapes de la différenciation des cellules souches hématopoïétiques. Pour ce faire, des cellules CD34+ purifiées à partir de sang de cordon ombilical humain ont été soumises à différentes stratégies d’analyse au cours d’une période de culture s’étalant de 0h à 96h. Dans un premier temps, le couplage des profils transcriptomiques en cellule unique (90 gènes) avec des données issues de microscopie time-lapse a permis de caractériser une phase de fluctuation transcriptomique et morphologique. Dans un second temps, nous avons confronté les transcriptomes de cellules uniques avec le profil d’accessibilité chromatinien en population. Cette analyse a mis en évidence l’importante composante stochastique qui précède l’engagement des cellules, ainsi que les modalités selon lesquelles une stabilisation de l'expression génique peut s’effectuer. Enfin, la combinaison de l’analyse métabolomique, couplée à l’étude de la dynamique de prolifération a mis en évidence l’action précoce du métabolisme, ainsi que son rôle potentiel dans la sélection phénotypique. Mes résultats s’inscrivent dans la liste des travaux questionnant la conception traditionnellement hiérarchique et univoque du processus de différenciation cellulaire. En particulier, la considération des fluctuations aléatoires de l’expression génique, ainsi que la caractérisation d’un lien étroit entre le métabolisme et la régulation épigénétique, sont tous deux des éléments m’ayant permis de proposer un nouveau modèle de la différenciation cellulaire.My thesis work explores the link between metabolism, chromatin state and transcription during the early stages of hematopoietic stem cell differentiation. In order to achieve this, CD34+ cells were purified from human umbilical cord blood and underwent different analysis strategies during a culture period ranging from 0h to 96h. First, the coupling of transcriptomic profiles in single cells (90 genes) with data from time-lapse microscopy revealed a phase of transcriptomic and morphological fluctuation. Secondly, we compared the transcriptomes of single cells with bulk chromatin accessibility profile. This analysis highlighted the important stochastic component that precedes cell engagement, as well as the ways in which gene expression stabilization can occur. Finally, the combination of metabolomic analysis, coupled with the study of proliferation dynamics, highlighted the early action of metabolism, as well as its potential role in phenotypic selection. My results are part of the list of works that have come to question the traditionally hierarchical and unambiguous conception of the process of cell differentiation. In particular, the consideration of random fluctuations in gene expression, as well as the characterization of a close link between metabolism and epigenetic regulation, both have allowed me to propose a new model of cell differentiation

Étude de la dynamique métabolique et épigénétique des cellules souches hématopoïétiques au cours du processus de différenciation cellulaire

My thesis work explores the link between metabolism, chromatin state and transcription during the early stages of hematopoietic stem cell differentiation. In order to achieve this, CD34+ cells were purified from human umbilical cord blood and underwent different analysis strategies during a culture period ranging from 0h to 96h. First, the coupling of transcriptomic profiles in single cells (90 genes) with data from time-lapse microscopy revealed a phase of transcriptomic and morphological fluctuation. Secondly, we compared the transcriptomes of single cells with bulk chromatin accessibility profile. This analysis highlighted the important stochastic component that precedes cell engagement, as well as the ways in which gene expression stabilization can occur. Finally, the combination of metabolomic analysis, coupled with the study of proliferation dynamics, highlighted the early action of metabolism, as well as its potential role in phenotypic selection. My results are part of the list of works that have come to question the traditionally hierarchical and unambiguous conception of the process of cell differentiation. In particular, the consideration of random fluctuations in gene expression, as well as the characterization of a close link between metabolism and epigenetic regulation, both have allowed me to propose a new model of cell differentiation.Mes travaux de thèse explorent l’articulation entre le métabolisme, l’état chromatinien et la transcription au cours des premières étapes de la différenciation des cellules souches hématopoïétiques. Pour ce faire, des cellules CD34+ purifiées à partir de sang de cordon ombilical humain ont été soumises à différentes stratégies d’analyse au cours d’une période de culture s’étalant de 0h à 96h. Dans un premier temps, le couplage des profils transcriptomiques en cellule unique (90 gènes) avec des données issues de microscopie time-lapse a permis de caractériser une phase de fluctuation transcriptomique et morphologique. Dans un second temps, nous avons confronté les transcriptomes de cellules uniques avec le profil d’accessibilité chromatinien en population. Cette analyse a mis en évidence l’importante composante stochastique qui précède l’engagement des cellules, ainsi que les modalités selon lesquelles une stabilisation de l'expression génique peut s’effectuer. Enfin, la combinaison de l’analyse métabolomique, couplée à l’étude de la dynamique de prolifération a mis en évidence l’action précoce du métabolisme, ainsi que son rôle potentiel dans la sélection phénotypique. Mes résultats s’inscrivent dans la liste des travaux questionnant la conception traditionnellement hiérarchique et univoque du processus de différenciation cellulaire. En particulier, la considération des fluctuations aléatoires de l’expression génique, ainsi que la caractérisation d’un lien étroit entre le métabolisme et la régulation épigénétique, sont tous deux des éléments m’ayant permis de proposer un nouveau modèle de la différenciation cellulaire

Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterising transcriptional changes in cord blood-derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the 2 stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the 2 phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology, and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process (which is different from a simple binary switch between 2 options, as it is usually envisioned)

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis

Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells

When human cord blood-derived CD34+ cells are induced to differentiate, they undergo rapid and dynamic morphological and molecular transformations that are critical for fate commitment. In particular, the cells pass through a transitory phase known as "multilineage-primed" state. These cells are characterized by a mixed gene expression profile, different in each cell, with the coexpression of many genes characteristic for concurrent cell lineages. The aim of our study is to understand the mechanisms of the establishment and the exit from this transitory state. We investigated this issue using single-cell RNA sequencing and ATAC-seq. Two phases were detected. The first phase is a rapid and global chromatin decompaction that makes most of the gene promoters in the genome accessible for transcription. It results 24 h later in enhanced and pervasive transcription of the genome leading to the concomitant increase in the cell-to-cell variability of transcriptional profiles. The second phase is the exit from the multilineage-primed phase marked by a slow chromatin closure and a subsequent overall down-regulation of gene transcription. This process is selective and results in the emergence of coherent expression profiles corresponding to distinct cell subpopulations. The typical time scale of these events spans 48 to 72 h. These observations suggest that the nonspecificity of genome decompaction is the condition for the generation of a highly variable multilineage expression profile. The nonspecific phase is followed by specific regulatory actions that stabilize and maintain the activity of key genes, while the rest of the genome becomes repressed again by the chromatin recompaction. Thus, the initiation of differentiation is reminiscent of a constrained optimization process that associates the spontaneous generation of gene expression diversity to subsequent regulatory actions that maintain the activity of some genes, while the rest of the genome sinks back to the repressive closed chromatin state.ISSN:1544-9173ISSN:1545-788

Single-cell gene expression in ‘high’, ‘medium’, and ‘low’ CD133 cells.

<p>(A) t-stochastic neighbour embedding (t-SNE) map of single-cell transcriptional data. Each point represents a single cell highlighted in a different colour for ‘high’, ‘medium’, and ‘low’ CD133 cells. ‘High’ and ‘low’ cells are in separated clusters corresponding to cluster #1 and #2 in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1B</a>. ‘Medium’ CD133 cells are distributed in and between these 2 clusters, indicating their intermediate character. (B) Scatter plot representation of PU1 and GATA1 expression in individual cells of the ‘high’, ‘medium’, and ‘low’ CD133 fraction. Note that GATA1 is not expressed in ‘high’ cells. Coexpression of the 2 genes is observed only in some ‘medium’ and ‘low’ cells. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p

Quantitative analysis of dynamic phenotypes as determined by time-lapse data.

<p>(A) Association between the morphology, switch frequency, cell cycle length, and the type of cell divisions of second- and third-generation cells. Each point represents a single cell. Siblings with different dynamic behaviour and morphology (in green) are usually characterised by high switch frequencies. Siblings with similar dynamic behaviour and morphologies are shown in blue. The morphology is given as a ratio of time spent in round/polarised shape by a cell during the cell cycle. Switch frequency is given in number of morphological transformations per hour. Cell cycle length is in hours. (B) Dynamic phenotype change during the first 2 cell divisions as determined on the basis of time-lapse records. Three different dynamic phenotypes were identified: stable polarised, frequent switchers, and stable round. Cells tended to transmit dynamic phenotypes to daughter cells during cell division. Polarised and frequent switchers produced round cells, and frequent switchers were always produced by polarised mothers. Phenotypic change is not associated with asymmetric division; it can occur at any time in the cell cycle. Since round cells always produce round daughters, the whole process is biased and the proportion of this phenotype increases. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s012" target="_blank">S2 Data</a>.)</p

Transcriptional profile of cord blood-derived CD34+ cells treated with valproic acid (VPA) at t = 0 h, t = 24 h, t = 48 h, and t = 72 h after the beginning of the experiment as compared to untreated, normal control cells.

<p>(A) A cytometric analysis of the effect of VPA on cord blood CD34+ cells shows an increase in the CD90 protein in most cells, while the CD34 and CD38 markers remain essentially unchanged. (B) Heat map representation of the expression levels of 90 genes as determined by single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR) in VPA-treated cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h. The colour codes for the time points of cells are indicated on the right; the colour codes for expression levels are indicated below the heat map. Note the high heterogeneity and lack of clear clustering of the expression patterns. (C) t-distributed stochastic neighbour embedding (t-SNE) plot representation of transcription data obtained for VPA-treated cells compared to untreated normal cells (data for these cells are the same as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1</a>). The gene expression data obtained in the 2 experiments were mapped together. Each point represents a single cell, and the cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h are highlighted separately in the 4 panels. The colour codes for VPA-treated (+VPA) and VPA-untreated (−VPA) are indicated below the panels. Clusters #1 and #2, identified at t = 48 h and t = 72 h in −VPA cells (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1</a>), are indicated on the t = 72 h panel. Note the clear separation of the +VPA and −VPA cells at every time point except t = 24 h. Note also that +VPA cells do not contribute to clusters #1 and #2, indicating that they do not acquire expression profiles typical of these cells. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p

Transcriptional profile of cord blood-derived CD34+ cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h after the beginning of the experiment.

<p>(A) CD34+ cells were isolated from human cord blood and cultured in serum-free medium with early acting cytokines. Single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to analyse single-cell transcription at 0 h, 24 h, 48 h and 72 h. At the same time, individual clones were continuously monitored using time-lapse microscopy. (B) t-distributed stochastic neighbour embedding (t-SNE) map of single-cell transcription data. The 4 panels show analysis of the same data set, with each point representing a single cell highlighted in different colours depending on the given time point. The 2 clusters identified by gap statistics at t = 48 h and t = 72 h are surrounded by an ellipse and numbered #1 and #2 for multipotent and common myeloid progenitor (CMP)-like cells. Note the rapid evolution of the expression profiles. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.) (C) A heat map representation of the expression levels of a subset of genes that strongly contributed to the differentiation of the different groups (as detected by principal component analysis [PCA]; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s002" target="_blank">S2 Fig</a>) and cluster analysis of expression profiles at the different time points show the rapid evolution of gene expression. The list of the genes used (shown on the right) includes well-known genes acting during hematopoietic differentiation but also many randomly selected genes. The colour code for expression levels is indicated below. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.) (D) Pairwise correlations between the genes analysed using single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR). Only the gene pairs with a Pearson correlation coefficient higher than 0.8 are indicated for each time point. The 2 clusters identified at t = 48 h and t = 72 h are represented separately. Note the transient increase of the average correlation in cluster #2 at t = 48 h, indicating a state transition. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p