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Functional tissue units and their primary tissue motifs in multi-scale physiology.

de Bono B, Grenon P, Baldock R, Hunter P - J Biomed Semantics (2013)

Bottom Line: These approaches have not significantly facilitated the general integration of tissue- and molecular-level knowledge across the board in support of a systematic classification of tissue function, as well as the coherent multi-scale study of physiology.In our work, we outline the biophysical rationale for a rigorous definition of a unit of functional tissue organization, and demonstrate the application of primary motifs in tissue classification.In so doing, we acknowledge (i) the fundamental role of capillaries in directing and radically informing tissue architecture, as well as (ii) the importance of taking into full account the critical influence of neighbouring cellular environments when studying complex developmental and pathological phenomena.

View Article: PubMed Central - HTML - PubMed

Affiliation: Auckland Bioengineering Institute, University of Auckland, Symonds Street, Auckland 1010, New Zealand. b.bono@ucl.ac.uk.

ABSTRACT

Background: Histology information management relies on complex knowledge derived from morphological tissue analyses. These approaches have not significantly facilitated the general integration of tissue- and molecular-level knowledge across the board in support of a systematic classification of tissue function, as well as the coherent multi-scale study of physiology. Our work aims to support directly these integrative goals.

Results: We describe, for the first time, the precise biophysical and topological characteristics of functional units of tissue. Such a unit consists of a three-dimensional block of cells centred around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block. We refer to this block as a functional tissue unit. As a means of simplifying the knowledge representation of this unit, and rendering this knowledge more amenable to automated reasoning and classification, we developed a simple descriptor of its cellular content and anatomical location, which we refer to as a primary tissue motif. In particular, a primary motif captures the set of cellular participants of diffusion-mediated interactions brokered by secreted products to create a tissue-level molecular network.

Conclusions: Multi-organ communication, therefore, may be interpreted in terms of interactions between molecular networks housed by interconnected functional tissue units. By extension, a functional picture of an organ, or its tissue components, may be rationally assembled using a collection of these functional tissue units as building blocks. In our work, we outline the biophysical rationale for a rigorous definition of a unit of functional tissue organization, and demonstrate the application of primary motifs in tissue classification. In so doing, we acknowledge (i) the fundamental role of capillaries in directing and radically informing tissue architecture, as well as (ii) the importance of taking into full account the critical influence of neighbouring cellular environments when studying complex developmental and pathological phenomena.

No MeSH data available.


Step-by-step example illustrating the automation of primary tissue motif comparison. [A] FTU knowledge about 5 distinct tissues (in this particular example, derived from histology textbooks[16,17]) generated lists of distinct constituent cell types for each of the corresponding derivative primary tissue motifs. [B] Each distinct cell type in [A] was mapped to the equivalent term from the CellType ontology and assigned its unique term ID. [C] An all-vs-all pairwise comparison between the primary tissue motifs (ptm) was carried out as follows: (i) for every unique combination of ptm pairs (such that a pair consists of ptm_X and ptm_Y), an all-vs-all semantic similarity score c() for each unique combination of CellType term pairs is calculated (such that one CellType term is drawn from ptm_X and another from ptm_Y); (ii) the set p{} of highest scoring exclusive pairs of CellType terms is identified – exclusivity in a pair ensures that, once a CellType term from one ptm is selected to match another CellType term from another ptm, neither of these two CellType terms are included in any other pair in p{}; (iii) the sum of c() scores in p{} are divided by the average number of cell types across ptm_X and ptm_Y to generate s(ptm_X,ptm_Y). [D] The set of ptm elements is clustered over the pairwise score s(). See also Table 1 for concrete values of c(), p{} and s() involving the 5 distinct tissues referred to in [A].
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Figure 2: Step-by-step example illustrating the automation of primary tissue motif comparison. [A] FTU knowledge about 5 distinct tissues (in this particular example, derived from histology textbooks[16,17]) generated lists of distinct constituent cell types for each of the corresponding derivative primary tissue motifs. [B] Each distinct cell type in [A] was mapped to the equivalent term from the CellType ontology and assigned its unique term ID. [C] An all-vs-all pairwise comparison between the primary tissue motifs (ptm) was carried out as follows: (i) for every unique combination of ptm pairs (such that a pair consists of ptm_X and ptm_Y), an all-vs-all semantic similarity score c() for each unique combination of CellType term pairs is calculated (such that one CellType term is drawn from ptm_X and another from ptm_Y); (ii) the set p{} of highest scoring exclusive pairs of CellType terms is identified – exclusivity in a pair ensures that, once a CellType term from one ptm is selected to match another CellType term from another ptm, neither of these two CellType terms are included in any other pair in p{}; (iii) the sum of c() scores in p{} are divided by the average number of cell types across ptm_X and ptm_Y to generate s(ptm_X,ptm_Y). [D] The set of ptm elements is clustered over the pairwise score s(). See also Table 1 for concrete values of c(), p{} and s() involving the 5 distinct tissues referred to in [A].

Mentions: Our method derives a pairwise similarity score s() for the comparison of cellular constituents in a primary tissue motif. The results of function s() provide the means to classify FTUs by clustering over this score. The key steps carried out in this method are exemplified below, by way of demonstration, and correspondingly illustrated in Figure 2:


Functional tissue units and their primary tissue motifs in multi-scale physiology.

de Bono B, Grenon P, Baldock R, Hunter P - J Biomed Semantics (2013)

Step-by-step example illustrating the automation of primary tissue motif comparison. [A] FTU knowledge about 5 distinct tissues (in this particular example, derived from histology textbooks[16,17]) generated lists of distinct constituent cell types for each of the corresponding derivative primary tissue motifs. [B] Each distinct cell type in [A] was mapped to the equivalent term from the CellType ontology and assigned its unique term ID. [C] An all-vs-all pairwise comparison between the primary tissue motifs (ptm) was carried out as follows: (i) for every unique combination of ptm pairs (such that a pair consists of ptm_X and ptm_Y), an all-vs-all semantic similarity score c() for each unique combination of CellType term pairs is calculated (such that one CellType term is drawn from ptm_X and another from ptm_Y); (ii) the set p{} of highest scoring exclusive pairs of CellType terms is identified – exclusivity in a pair ensures that, once a CellType term from one ptm is selected to match another CellType term from another ptm, neither of these two CellType terms are included in any other pair in p{}; (iii) the sum of c() scores in p{} are divided by the average number of cell types across ptm_X and ptm_Y to generate s(ptm_X,ptm_Y). [D] The set of ptm elements is clustered over the pairwise score s(). See also Table 1 for concrete values of c(), p{} and s() involving the 5 distinct tissues referred to in [A].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126067&req=5

Figure 2: Step-by-step example illustrating the automation of primary tissue motif comparison. [A] FTU knowledge about 5 distinct tissues (in this particular example, derived from histology textbooks[16,17]) generated lists of distinct constituent cell types for each of the corresponding derivative primary tissue motifs. [B] Each distinct cell type in [A] was mapped to the equivalent term from the CellType ontology and assigned its unique term ID. [C] An all-vs-all pairwise comparison between the primary tissue motifs (ptm) was carried out as follows: (i) for every unique combination of ptm pairs (such that a pair consists of ptm_X and ptm_Y), an all-vs-all semantic similarity score c() for each unique combination of CellType term pairs is calculated (such that one CellType term is drawn from ptm_X and another from ptm_Y); (ii) the set p{} of highest scoring exclusive pairs of CellType terms is identified – exclusivity in a pair ensures that, once a CellType term from one ptm is selected to match another CellType term from another ptm, neither of these two CellType terms are included in any other pair in p{}; (iii) the sum of c() scores in p{} are divided by the average number of cell types across ptm_X and ptm_Y to generate s(ptm_X,ptm_Y). [D] The set of ptm elements is clustered over the pairwise score s(). See also Table 1 for concrete values of c(), p{} and s() involving the 5 distinct tissues referred to in [A].
Mentions: Our method derives a pairwise similarity score s() for the comparison of cellular constituents in a primary tissue motif. The results of function s() provide the means to classify FTUs by clustering over this score. The key steps carried out in this method are exemplified below, by way of demonstration, and correspondingly illustrated in Figure 2:

Bottom Line: These approaches have not significantly facilitated the general integration of tissue- and molecular-level knowledge across the board in support of a systematic classification of tissue function, as well as the coherent multi-scale study of physiology.In our work, we outline the biophysical rationale for a rigorous definition of a unit of functional tissue organization, and demonstrate the application of primary motifs in tissue classification.In so doing, we acknowledge (i) the fundamental role of capillaries in directing and radically informing tissue architecture, as well as (ii) the importance of taking into full account the critical influence of neighbouring cellular environments when studying complex developmental and pathological phenomena.

View Article: PubMed Central - HTML - PubMed

Affiliation: Auckland Bioengineering Institute, University of Auckland, Symonds Street, Auckland 1010, New Zealand. b.bono@ucl.ac.uk.

ABSTRACT

Background: Histology information management relies on complex knowledge derived from morphological tissue analyses. These approaches have not significantly facilitated the general integration of tissue- and molecular-level knowledge across the board in support of a systematic classification of tissue function, as well as the coherent multi-scale study of physiology. Our work aims to support directly these integrative goals.

Results: We describe, for the first time, the precise biophysical and topological characteristics of functional units of tissue. Such a unit consists of a three-dimensional block of cells centred around a capillary, such that each cell in this block is within diffusion distance from any other cell in the same block. We refer to this block as a functional tissue unit. As a means of simplifying the knowledge representation of this unit, and rendering this knowledge more amenable to automated reasoning and classification, we developed a simple descriptor of its cellular content and anatomical location, which we refer to as a primary tissue motif. In particular, a primary motif captures the set of cellular participants of diffusion-mediated interactions brokered by secreted products to create a tissue-level molecular network.

Conclusions: Multi-organ communication, therefore, may be interpreted in terms of interactions between molecular networks housed by interconnected functional tissue units. By extension, a functional picture of an organ, or its tissue components, may be rationally assembled using a collection of these functional tissue units as building blocks. In our work, we outline the biophysical rationale for a rigorous definition of a unit of functional tissue organization, and demonstrate the application of primary motifs in tissue classification. In so doing, we acknowledge (i) the fundamental role of capillaries in directing and radically informing tissue architecture, as well as (ii) the importance of taking into full account the critical influence of neighbouring cellular environments when studying complex developmental and pathological phenomena.

No MeSH data available.