Dataset Information

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation.

ABSTRACT: Understanding the emergence of lymphoid committed cells from multipotent progenitors (MPP) is a great challenge in hematopoiesis. To gain deeper insight into the dynamic expression changes associated with these transitions, we report the quantitative transcriptome of two MPP subsets and the common lymphoid progenitor (CLP). While the transcriptome is rather stable between MPP2 and MPP3, expression changes increase with differentiation. Among those, we found that pioneer lymphoid genes such as Rag1, Mpeg1, and Dntt are expressed continuously from MPP2. Others, such as CD93, are CLP specific, suggesting their potential use as new markers to improve purification of lymphoid populations. Notably, a six-transcription factor network orchestrates the lymphoid differentiation program. Additionally, we pinpointed 24 long intergenic-non-coding RNA (lincRNA) differentially expressed through commitment and further identified seven novel forms. Collectively, our approach provides a comprehensive landscape of coding and non-coding transcriptomes expressed during lymphoid commitment.

SUBMITTER: Chalabi S

PROVIDER: S-EPMC8834919 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation.

Chalabi Smahane S Legrand Agnes A Michaels Victoria V Palomares Marie-Ange MA Olaso Robert R Boland Anne A Deleuze Jean-François JF Ezine Sophie S Battail Christophe C Tronik-Le Roux Diana D

International journal of molecular sciences 20220120 3

Understanding the emergence of lymphoid committed cells from multipotent progenitors (MPP) is a great challenge in hematopoiesis. To gain deeper insight into the dynamic expression changes associated with these transitions, we report the quantitative transcriptome of two MPP subsets and the common lymphoid progenitor (CLP). While the transcriptome is rather stable between MPP2 and MPP3, expression changes increase with differentiation. Among those, we found that pioneer lymphoid genes such as <i ...[more]

PMID: 35163045

Similar Datasets

Project description:BackgroundUnderstanding the mechanisms controlling stem cell differentiation is the key to future advances in tissue and organ regeneration. Embryonic stem (ES) cell differentiation can be triggered by embryoid body (EB) formation, which involves ES cell aggregation in suspension. EB growth in the absence of leukaemia inhibitory factor (LIF) leads EBs to mimic early embryonic development, giving rise to markers representative of endoderm, mesoderm and ectoderm. Here, we have used microarrays to investigate differences in gene expression between 3 undifferentiated ES cell lines, and also between undifferentiated ES cells and Day 1-4 EBs.ResultsAn initial array study identified 4 gene expression changes between 3 undifferentiated ES cell lines. Tissue culture conditions for ES differentiation were then optimized to give the maximum range of gene expression and growth. -Undifferentiated ES cells and EBs cultured with and without LIF at each day for 4 days were subjected to microarray analysis. -Differential expression of 23 genes was identified. 13 of these were also differentially regulated in a separate array comparison between undifferentiated ES cells and compartments of very early embryos. A high degree of inter-replicate variability was noted when confirming array results. Using a panel of marker genes, RNA amplification and RT-PCR, we examined expression pattern variation between individual -D4-Lif EBs. We found that individual EBs selected from the same dish were highly variable in gene expression profile.ConclusionES cell lines derived from different mouse strains and carrying different genetic modifications are almost invariant in gene expression profile under conditions used to maintain pluripotency. Tissue culture conditions that give the widest range of gene expression and maximise EB growth involve the use of 20% serum and starting cell numbers of 1000 per EB. 23 genes of importance to early development have been identified; more than half of these are also identified using similar studies, thus validating our results. EBs cultured in the same dish vary widely in terms of their gene expression (and hence, undoubtedly, in their future differentiation potential). This may explain some of the inherent variability in differentiation protocols that use EBs.

Project description:BackgroundMacrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns.ResultsWe globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models.ConclusionThree macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

Dataset Information

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation.

Publications

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets