Limits...
On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis.

Weis CA, Grießmann BW, Scharff C, Detzner C, Pfister E, Marx A, Zoellner FG - Diagn Pathol (2015)

Bottom Line: In contrast to multicolour staining (e.g. 10-colour immunofluorescence) the financial and technical requirements are fairly minor.Second, the approach allows searching for different types of spatial interactions (e.g. direct and indirect cellular interaction) between objects by taking field shape into account (e.g. thin vs. broad).Third, by describing spatially distributed groups of objects as summation field, it gives cluster definition that relies rather on the bare object distance than on the modelled spatial cellular interaction.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Cleo-Aron.Weis@medma.uni-heidelberg.de.

ABSTRACT

Background: Immunohistochemical analysis of cellular interactions in the bone marrow in situ is demanding, due to its heterogeneous cellular composition, the poor delineation and overlap of functional compartments and highly complex immunophenotypes of several cell populations (e.g. regulatory T-cells) that require immunohistochemical marker sets for unambiguous characterization. To overcome these difficulties, we herein present an approach to describe objects (e.g. cells, bone trabeculae) by a scalar field that can be propagated through registered images of serial histological sections.

Methods: The transformation of objects within images (e.g. cells) to a scalar field was performed by convolution of the object's centroids with differently formed radial basis function (e.g. for direct or indirect spatial interaction). On the basis of such a scalar field, a summation field described distributed objects within an image.

Results: After image registration i) colocalization analysis could be performed on basis scalar field, which is propagated through registered images, and - due to the shape of the field - were barely prone to matching errors and morphological changes by different cutting levels; ii) furthermore, depending on the field shape the colocalization measurements could also quantify spatial interaction (e.g. direct or paracrine cellular contact); ii) the field-overlap, which represents the spatial distance, of different objects (e.g. two cells) could be calculated by the histogram intersection.

Conclusions: The description of objects (e.g. cells, cell clusters, bone trabeculae etc.) as a field offers several possibilities: First, co-localization of different markers (e.g. by immunohistochemical staining) in serial sections can be performed in an automatic, objective and quantifiable way. In contrast to multicolour staining (e.g. 10-colour immunofluorescence) the financial and technical requirements are fairly minor. Second, the approach allows searching for different types of spatial interactions (e.g. direct and indirect cellular interaction) between objects by taking field shape into account (e.g. thin vs. broad). Third, by describing spatially distributed groups of objects as summation field, it gives cluster definition that relies rather on the bare object distance than on the modelled spatial cellular interaction.

No MeSH data available.


Related in: MedlinePlus

Comparison of the colocalization via RBF to the standard colocalization analysis in fluorescence microscopy. The golden standard of colocalization analysis in fluorescence microscopy is to analyse the different colour channels (e.g. red and green). In this figure we simply matched this method to IHC by comparing and analysing the “brown” channel obtained by colour deconvolution. One small region of a slide sequentially stained for CD61 (A) and MPO (A) was analysed. Subsequently, colocalization on basis of the herein described RBF was compared to analysis of colour chancels. a One small region with one CD61-positive megakaryocyte (upper image) was compared to the same region shifted one cell width to left (middle image). The upper scatter plot shows the brown colour channels of the images plotted against each other, whereas the lower one shows the scatter plot for the RBF. b The same small region now stained for MPO was used to check for colocalization/direct spatial interaction of the CD61-positive megakaryocyte and the MPO-positive granulocytes (upper image). The arrow in the zoomed part of the image highlight direct spatial interaction of the megakaryocyte and the granulocytes. The upper scatter plot again shows the brown colour channels plotted against each other and the lower one shows the RBFs plotted against each other
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4557224&req=5

Fig3: Comparison of the colocalization via RBF to the standard colocalization analysis in fluorescence microscopy. The golden standard of colocalization analysis in fluorescence microscopy is to analyse the different colour channels (e.g. red and green). In this figure we simply matched this method to IHC by comparing and analysing the “brown” channel obtained by colour deconvolution. One small region of a slide sequentially stained for CD61 (A) and MPO (A) was analysed. Subsequently, colocalization on basis of the herein described RBF was compared to analysis of colour chancels. a One small region with one CD61-positive megakaryocyte (upper image) was compared to the same region shifted one cell width to left (middle image). The upper scatter plot shows the brown colour channels of the images plotted against each other, whereas the lower one shows the scatter plot for the RBF. b The same small region now stained for MPO was used to check for colocalization/direct spatial interaction of the CD61-positive megakaryocyte and the MPO-positive granulocytes (upper image). The arrow in the zoomed part of the image highlight direct spatial interaction of the megakaryocyte and the granulocytes. The upper scatter plot again shows the brown colour channels plotted against each other and the lower one shows the RBFs plotted against each other

Mentions: The golden standard of colocalization analysis in fluorescence microscopy is to analyse and plot against each other the different colour channels (e.g. red and green) [38]. By comparing and analysing the “brown” channel obtained by colour deconvolution [28], we tried to adapt this approach to IHC images and subsequently compared the results of both methods. First we compared the results for analysis of one CD61-positive megakaryocyte translated one cell width to left (Fig. 3a): Whereas parts of the point cloud are arranged around the bisecting line (Fig. 3a upper scatter plot), the density function-based analysis shows a clearly forked point cloud (lower scatter plot). Second we analysed the same region now stained for MPO by sequential IHC [17] for colocalization of megakaryocytes and granulocytes (Fig. 3b): The point cloud for the colour channel analysis is mostly distributed around the bisecting line (Fig. 3b upper scatter plot). This does not fit to the images where the brown colour is placed in different areas (one brown megakaryocyte in A and many brown granulocytes in B). In comparison, the lower scatter plot shows that there is mostly no correlation between the CD61- and the MPO-positive cells. However, there is one branch of the point cloud that is due to the direct spatial contact of cell populations (Fig. 3b white arrows).Fig. 3


On the representation of cells in bone marrow pathology by a scalar field: propagation through serial sections, co-localization and spatial interaction analysis.

Weis CA, Grießmann BW, Scharff C, Detzner C, Pfister E, Marx A, Zoellner FG - Diagn Pathol (2015)

Comparison of the colocalization via RBF to the standard colocalization analysis in fluorescence microscopy. The golden standard of colocalization analysis in fluorescence microscopy is to analyse the different colour channels (e.g. red and green). In this figure we simply matched this method to IHC by comparing and analysing the “brown” channel obtained by colour deconvolution. One small region of a slide sequentially stained for CD61 (A) and MPO (A) was analysed. Subsequently, colocalization on basis of the herein described RBF was compared to analysis of colour chancels. a One small region with one CD61-positive megakaryocyte (upper image) was compared to the same region shifted one cell width to left (middle image). The upper scatter plot shows the brown colour channels of the images plotted against each other, whereas the lower one shows the scatter plot for the RBF. b The same small region now stained for MPO was used to check for colocalization/direct spatial interaction of the CD61-positive megakaryocyte and the MPO-positive granulocytes (upper image). The arrow in the zoomed part of the image highlight direct spatial interaction of the megakaryocyte and the granulocytes. The upper scatter plot again shows the brown colour channels plotted against each other and the lower one shows the RBFs plotted against each other
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4557224&req=5

Fig3: Comparison of the colocalization via RBF to the standard colocalization analysis in fluorescence microscopy. The golden standard of colocalization analysis in fluorescence microscopy is to analyse the different colour channels (e.g. red and green). In this figure we simply matched this method to IHC by comparing and analysing the “brown” channel obtained by colour deconvolution. One small region of a slide sequentially stained for CD61 (A) and MPO (A) was analysed. Subsequently, colocalization on basis of the herein described RBF was compared to analysis of colour chancels. a One small region with one CD61-positive megakaryocyte (upper image) was compared to the same region shifted one cell width to left (middle image). The upper scatter plot shows the brown colour channels of the images plotted against each other, whereas the lower one shows the scatter plot for the RBF. b The same small region now stained for MPO was used to check for colocalization/direct spatial interaction of the CD61-positive megakaryocyte and the MPO-positive granulocytes (upper image). The arrow in the zoomed part of the image highlight direct spatial interaction of the megakaryocyte and the granulocytes. The upper scatter plot again shows the brown colour channels plotted against each other and the lower one shows the RBFs plotted against each other
Mentions: The golden standard of colocalization analysis in fluorescence microscopy is to analyse and plot against each other the different colour channels (e.g. red and green) [38]. By comparing and analysing the “brown” channel obtained by colour deconvolution [28], we tried to adapt this approach to IHC images and subsequently compared the results of both methods. First we compared the results for analysis of one CD61-positive megakaryocyte translated one cell width to left (Fig. 3a): Whereas parts of the point cloud are arranged around the bisecting line (Fig. 3a upper scatter plot), the density function-based analysis shows a clearly forked point cloud (lower scatter plot). Second we analysed the same region now stained for MPO by sequential IHC [17] for colocalization of megakaryocytes and granulocytes (Fig. 3b): The point cloud for the colour channel analysis is mostly distributed around the bisecting line (Fig. 3b upper scatter plot). This does not fit to the images where the brown colour is placed in different areas (one brown megakaryocyte in A and many brown granulocytes in B). In comparison, the lower scatter plot shows that there is mostly no correlation between the CD61- and the MPO-positive cells. However, there is one branch of the point cloud that is due to the direct spatial contact of cell populations (Fig. 3b white arrows).Fig. 3

Bottom Line: In contrast to multicolour staining (e.g. 10-colour immunofluorescence) the financial and technical requirements are fairly minor.Second, the approach allows searching for different types of spatial interactions (e.g. direct and indirect cellular interaction) between objects by taking field shape into account (e.g. thin vs. broad).Third, by describing spatially distributed groups of objects as summation field, it gives cluster definition that relies rather on the bare object distance than on the modelled spatial cellular interaction.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Cleo-Aron.Weis@medma.uni-heidelberg.de.

ABSTRACT

Background: Immunohistochemical analysis of cellular interactions in the bone marrow in situ is demanding, due to its heterogeneous cellular composition, the poor delineation and overlap of functional compartments and highly complex immunophenotypes of several cell populations (e.g. regulatory T-cells) that require immunohistochemical marker sets for unambiguous characterization. To overcome these difficulties, we herein present an approach to describe objects (e.g. cells, bone trabeculae) by a scalar field that can be propagated through registered images of serial histological sections.

Methods: The transformation of objects within images (e.g. cells) to a scalar field was performed by convolution of the object's centroids with differently formed radial basis function (e.g. for direct or indirect spatial interaction). On the basis of such a scalar field, a summation field described distributed objects within an image.

Results: After image registration i) colocalization analysis could be performed on basis scalar field, which is propagated through registered images, and - due to the shape of the field - were barely prone to matching errors and morphological changes by different cutting levels; ii) furthermore, depending on the field shape the colocalization measurements could also quantify spatial interaction (e.g. direct or paracrine cellular contact); ii) the field-overlap, which represents the spatial distance, of different objects (e.g. two cells) could be calculated by the histogram intersection.

Conclusions: The description of objects (e.g. cells, cell clusters, bone trabeculae etc.) as a field offers several possibilities: First, co-localization of different markers (e.g. by immunohistochemical staining) in serial sections can be performed in an automatic, objective and quantifiable way. In contrast to multicolour staining (e.g. 10-colour immunofluorescence) the financial and technical requirements are fairly minor. Second, the approach allows searching for different types of spatial interactions (e.g. direct and indirect cellular interaction) between objects by taking field shape into account (e.g. thin vs. broad). Third, by describing spatially distributed groups of objects as summation field, it gives cluster definition that relies rather on the bare object distance than on the modelled spatial cellular interaction.

No MeSH data available.


Related in: MedlinePlus