12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Basic quantitative morphological methods applied to the central nervous system

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Generating numbers has become an almost inevitable task associated with studies of the morphology of the nervous system. Numbers serve a desire for clarity and objectivity in the presentation of results and are a prerequisite for the statistical evaluation of experimental outcomes. Clarity, objectivity, and statistics make demands on the quality of the numbers that are not met by many methods. This review provides a refresher of problems associated with generating numbers that describe the nervous system in terms of the volumes, surfaces, lengths, and numbers of its components. An important aim is to provide comprehensible descriptions of the methods that address these problems. Collectively known as design‐based stereology, these methods share two features critical to their application. First, they are firmly based in mathematics and its proofs. Second and critically underemphasized, an understanding of their mathematical background is not necessary for their informed and productive application. Understanding and applying estimators of volume, surface, length or number does not require more of an organizational mastermind than an immunohistochemical protocol. And when it comes to calculations, square roots are the gravest challenges to overcome. Sampling strategies that are combined with stereological probes are efficient and allow a rational assessment if the numbers that have been generated are “good enough.” Much may be unfamiliar, but very little is difficult. These methods can no longer be scapegoats for discrepant results but faithfully produce numbers on the material that is assessed. They also faithfully reflect problems that associated with the histological material and the anatomically informed decisions needed to generate numbers that are not only valid in theory. It is within reach to generate practically useful numbers that must integrate with qualitative knowledge to understand the function of neural systems.

          Abstract

          Design‐based stereological methods allow the estimation of basic morphological parameters in representative samples of the sectioned central nervous system. Point probes can be used to estimate volume (illustrated for the Area Fraction Fractionator), line probes can be used to estimate surface area (illustrated for a vertical sections design), area probes can be used to estimate length (illustrated for the Spaceball probe), and volume probes must be used to estimate number (illustrated for the Disector). This review provides an introduction to how these methods solve problems associated with other quantitative approaches, how they are applied to histological material, how a sampling scheme can be designed and evaluated, and which practical problems need to be solved to generate the numbers that we will need to come to an understanding of central nervous system function.

          Related collections

          Most cited references251

          • Record: found
          • Abstract: found
          • Article: not found

          Unbiased stereological estimation of the total number of neurons in thesubdivisions of the rat hippocampus using the optical fractionator.

          A stereological method for obtaining estimates of the total number of neurons in five major subdivisions of the rat hippocampus is described. The new method, the optical fractionator, combines two recent developments in stereology: a three-dimensional probe for counting neuronal nuclei, the optical disector, and a systematic uniform sampling scheme, the fractionator. The optical disector results in unbiased estimates of neuron number, i.e., estimates that are free of assumptions about neuron size and shape, are unaffected by lost caps and overprojection, and approach the true number of neurons in an unlimited manner as the number of samples is increased. The fractionator involves sampling a known fraction of a structural component. In the case of neuron number, a zero dimensional quantity, it provides estimates that are unaffected by shrinkage before, during, and after processing of the tissue. Because the fractionator involves systematic sampling, it also results in highly efficient estimates. Typically only 100-200 neurons must be counted in an animal to obtain a precision that is compatible with experimental studies. The methodology is compared with those used in earlier works involving estimates of neuron number in the rat hippocampus and a number of new stereological methods that have particular relevance to the quantitative study of the structure of the nervous system are briefly described in an appendix.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Reconstruction and Simulation of Neocortical Microcircuitry.

            We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              NeuN, a neuronal specific nuclear protein in vertebrates.

              A battery of monoclonal antibodies (mAbs) against brain cell nuclei has been generated by repeated immunizations. One of these, mAb A60, recognizes a vertebrate nervous system- and neuron-specific nuclear protein that we have named NeuN (Neuronal Nuclei). The expression of NeuN is observed in most neuronal cell types throughout the nervous system of adult mice. However, some major cell types appear devoid of immunoreactivity including cerebellar Purkinje cells, olfactory bulb mitral cells, and retinal photoreceptor cells. NeuN can also be detected in neurons in primary cerebellar cultures and in retinoic acid-stimulated P19 embryonal carcinoma cells. Immunohistochemically detectable NeuN protein first appears at developmental timepoints which correspond with the withdrawal of the neuron from the cell cycle and/or with the initiation of terminal differentiation of the neuron. NeuN is a soluble nuclear protein, appears as 3 bands (46-48 x 10(3) M(r)) on immunoblots, and binds to DNA in vitro. The mAb crossreacts immunohistochemically with nervous tissue from rats, chicks, humans, and salamanders. This mAb and the protein recognized by it serve as an excellent marker for neurons in the central and peripheral nervous systems in both the embryo and adult, and the protein may be important in the determination of neuronal phenotype.
                Bookmark

                Author and article information

                Contributors
                lutz.slomianka@anatomy.uzh.ch
                Journal
                J Comp Neurol
                J Comp Neurol
                10.1002/(ISSN)1096-9861
                CNE
                The Journal of Comparative Neurology
                John Wiley & Sons, Inc. (Hoboken, USA )
                0021-9967
                1096-9861
                01 August 2020
                March 2021
                : 529
                : 4 ( doiID: 10.1002/cne.v529.4 )
                : 694-756
                Affiliations
                [ 1 ] University of Zürich, Institute of Anatomy Zürich Switzerland
                Author notes
                [*] [* ] Correspondence

                Lutz Slomianka, University of Zürich, Institute of Anatomy, Winterthurerstr 190, CH‐8057 Zürich, Switzerland.

                Email: lutz.slomianka@ 123456anatomy.uzh.ch

                Author information
                https://orcid.org/0000-0002-5402-0934
                Article
                CNE24976
                10.1002/cne.24976
                7818269
                32639600
                6685fc57-feae-4127-9097-6c539ad13a52
                © 2020 The Authors. The Journal of Comparative Neurology published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 31 January 2020
                : 15 June 2020
                : 16 June 2020
                Page count
                Figures: 46, Tables: 1, Pages: 63, Words: 55364
                Categories
                Review
                Review
                Custom metadata
                2.0
                March 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:21.01.2021

                Neurology
                quantitative morphology,design‐based stereology,number,length,surface,volume
                Neurology
                quantitative morphology, design‐based stereology, number, length, surface, volume

                Comments

                Comment on this article