Formation Of Different Tissues Research Articles

A spatial transcriptome map of the developing maize ear.

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

Mechanical explanation for simultaneous formation of different tissues in an organ morphogenesis

An interesting recent finding is that, in a medium of nearly linear elastic materials, the stem cell differentiation responds to the Young’s modulus of elasticity. It is discussed here that the more general mechanical identifier of differentiation is indeed the stiffness (rather than the Young’s modulus). Specifically, for nonlinear (and thus more realistic/physiologically mimetic) materials the stiffness experienced by the cells is affected by the intracellular distance and hence by the local density of the cells. It is proposed here that the stiffness-directed differentiation is affected not only by the substrate elasticity but also by the cell density and diffusion effects (relocations). Thus, it is suggested that nonlinear effects actually augment fascinating more versatile capabilities to the stiffness-directed differentiation compared to what was supposed by the traditional linear elastic materials. In particular, it is argued how such modified stiffness-directed differentiation criteria can explain simultaneous differentiation paths leading to the formation of an organ. Formation of organs like a limb requires simultaneous formation of different tissues from the hardest (central cartilage and bone) to softest ones (vascular networks of blood vessels and neurons) at correct locations. This means that while the pattern of initial stem cells is changing, they should follow different differentiation paths at the same time on the same substrate (a primary limb cross section, for instance). Here, it is shown that the distribution and relocation of the stem cells affect their sensing of the surrounding mechanics and hence their differentiation. This means various kinds of tissues automatically form, while the stem cells are redistributing and/or relocating.

Constructing temporal regulatory cascades in the context of development and cell differentiation

Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual web-tool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.

The connection between cellular mechanoregulation and tissue patterns during bone healing.

The formation of different tissues in the callus during secondary bone healing is at least partly influenced by mechanical stimuli. We use computer simulations to test the consequences of different hypotheses of the mechanoregulation at the cellular level on the patterns of tissues formed during healing. The computational study is based on an experiment on sheep, where after a tibial osteotomy, histological sections were harvested at different time points. In the simulations, we used a recently proposed basic phenomenological model, which allows ossification to occur either via endochondral or intramembranous ossification, but tries otherwise to employ a minimal number of simulation parameters. The model was extended to consider also the possibility of bone resorption and consequently allowing a description of the full healing progression till the restoration of the cortex. Specifically, we investigated how three changes in the mechanoregulation influence the resulting tissue patterns: (1) a time delay between stimulation of the cell and the formation of the tissue, (2) a variable mechanosensitivity of the cells, and (3) an independence of long time intervals of the soft tissue maturation from the mechanical stimulus. For all three scenarios, our simulations do not show qualitative differences in the time development of the tissue patterns. Largest differences were observed in the intermediate phases of healing in the amount and location of the cartilage. Interestingly, the course of healing was virtually unaltered in case of scenario (3) where tissue maturation proceeded independent of mechanical stimulation.

Mechanical microenvironments and protein expression associated with formation of different skeletal tissues during bone healing.

Uncovering the mechanisms of the sensitivity of bone healing to mechanical factors is critical for understanding the basic biology and mechanobiology of the skeleton, as well as for enhancing clinical treatment of bone injuries. This study refined an experimental method of measuring the strain microenvironment at the site of a bone injury during bone healing. This method used a rat model in which a well-controlled bending motion was applied to an osteotomy to induce the formation of pseudarthrosis that is composed of a range of skeletal tissues, including woven bone, cartilage, fibrocartilage, fibrous tissue, and clot tissue. The goal of this study was to identify both the features of the strain microenvironment associated with formation of these different tissues and the expression of proteins frequently implicated in sensing and transducing mechanical cues. By pairing the strain measurements with histological analyses that identified the regions in which each tissue type formed, we found that formation of the different tissue types occurs in distinct strain microenvironments and that the type of tissue formed is correlated most strongly to the local magnitudes of extensional and shear strains. Weaker correlations were found for dilatation. Immunohistochemical analyses of focal adhesion kinase and rho family proteins RhoA and CDC42 revealed differences within the cartilaginous tissues in the calluses from the pseudarthrosis model as compared to fracture calluses undergoing normal endochondral bone repair. These findings suggest the involvement of these proteins in the way by which mechanical stimuli modulate the process of cartilage formation during bone healing.

Runx1: no longer just for leukemia

Runx1: no longer just for leukemia

A challenge to the riddle of the carbohydrate chain

Abstract