Literature DB >> 2571514

The human locus coeruleus complex: an immunohistochemical and three dimensional reconstruction study.

K G Baker1, I Törk, J P Hornung, P Halasz.   

Abstract

The CA (catecholamine/catecholaminergic) cell populations of the locus coeruleus (LC) and subcoeruleus (SubC) were studied using serial sections of the human brainstem immunostained with an antibody against tyrosine hydroxylase. The tyrosine hydroxylase-immunoreactive (TH-IR) neurons were plotted in a computer reconstruction system and their number and soma size determined. Serial section computer analysis was then used to create a three dimensional reconstruction of the LC complex. The number of cells containing neuromelanin pigment was also determined and compared with the number of TH-IR cells. In our sample there were 53,900 TH-IR cells in the LC and a further 6260 cells in the SubC. These numbers were very similar to our estimates of the number of cells containing neuromelanin pigment and we concluded that virtually all of these cells were also tyrosine hydroxylase positive. The average soma size of the TH-IR cells of the LC was 37 microns and in the SubC 34 microns. In addition to these quantitative observations the morphology of the TH-IR and the Nissl stained cells is described in some detail. We also compared the groups of immunoreactive cells in the human pons with the noradrenergic groups A5-A7 described in the rat. Although in the human these groups are contiguous, A5 is not part of the LC complex. However we did find that the A7 group is equivalent to the rostroventral part of SubC while the remainder of SubC is formed by ventral A6.

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Year:  1989        PMID: 2571514     DOI: 10.1007/BF00274983

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  33 in total

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Review 10.  The locus coeruleus and its possible role in ageing and degenerative disease of the human central nervous system.

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  22 in total

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Review 6.  Olfaction in Parkinson's disease and related disorders.

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10.  Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

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