Stereological Analysis

  • Kristi M. Anderson
  • Adam M. Szlachetka
  • R. Lee MosleyEmail author
Part of the Springer Protocols Handbooks book series (SPH)


The use of stereology for quantitative estimates of biological features is becoming commonplace in modern laboratories. Over the past decades, stereology has evolved to keep pace with the ever-changing field of neuroscience. It has moved from its early applications for studying the geologic composition of rocks to an invaluable method to detect both small- and large-scale changes in the complex central nervous system. Recent developments in technology have made the practice highly efficient and accurate while providing a strategy to remove assumptions and investigator biases that lead to inaccurate numerical estimations. This chapter focuses on the use of stereology in neuroscience and provides evidence that validates the results generated via stereological analysis. The reader is introduced to the many applications of stereology in the laboratory including the quantification of neuron populations, measurement of dendrite and axon lengths, analysis of surface area of non-symmetrical shapes, and overall connectivity in the brain. This chapter provides systematic examples for the performance of a stereological study and is presented in a manner allowing the use of this chapter as a manual directly translatable for the neuroscience laboratory.


Stereology Designed-based quantitation CNS neurons Fractionators Disector 


  1. Abercrombie M (1946) Estimation of nuclear population from microtome sections. Anat Rec 94:239–247CrossRefPubMedGoogle Scholar
  2. Baddeley A (2001) Is stereology ‘unbiased’? Trends Neurosci 24:375–376, author reply 378–380CrossRefPubMedGoogle Scholar
  3. Boyce RW, Dorph-Petersen KA, Lyck L, Gundersen HJ (2010) Design-based stereology: introduction to basic concepts and practical approaches for estimation of cell number. Toxicol Pathol 38:1011–1025CrossRefPubMedGoogle Scholar
  4. Clarke R (1968) A comparative analysis of methods of estimating the size of cell populations from microtome sections. J R Microsc Soc 88:189–203CrossRefPubMedGoogle Scholar
  5. Coggeshall RE (2001) Commentary of the paper by Benes and Lange. Trends Neurosci 24:376–377, author reply 378–380CrossRefPubMedGoogle Scholar
  6. Geuna S (2000) Appreciating the difference between design-based and model-based sampling strategies in quantitative morphology of the nervous system. J Comp Neurol 427:333–339CrossRefPubMedGoogle Scholar
  7. Glaser JR, Glaser EM (2000) Stereology, morphometry, and mapping: the whole is greater than the sum of its parts. J Chem Neuroanat 20:115–126CrossRefPubMedGoogle Scholar
  8. Glaser J, Greene G, Hendricks S (2007) Stereology for biological research with a focus on neuroscience. MBF Bioscience, Williston, VTGoogle Scholar
  9. Graf W (1948) The microtome as an error producing factor in quantitative histological investigations. Acta Anat 6:14–44CrossRefGoogle Scholar
  10. Gundersen HJ, Boyce RW, Nyengaard JR, Odgaard A (1993) The conneulor: unbiased estimation of connectivity using physical disectors under projection. Bone 14:217–222CrossRefPubMedGoogle Scholar
  11. Hendry IA (1976) A method to correct adequately for the change in neuronal size when estimating neuronal numbers after nerve growth factor treatment. J Neurocytol 5:337–349CrossRefPubMedGoogle Scholar
  12. Howard CV, Sandau K (1992) Measuring the surface area of a cell by the method of the spatial grid with a CSLM—a demonstration. J Microsc 165:183–188CrossRefPubMedGoogle Scholar
  13. Kroustrup JP, Gundersen HJ (2001) Estimating the number of complex particles using the ConnEulor principle. J Microsc 203:314–320CrossRefPubMedGoogle Scholar
  14. Kubinova L, Janacek J (1998) Estimating surface area by the isotropic fakir method from thick slices cut in an arbitrary direction. J Microsc 191:201–211CrossRefPubMedGoogle Scholar
  15. Mayhew TM, Gundersen HJ (1996) If you assume, you can make an ass out of u and me: a decade of the disector for stereological counting of particles in 3D space. J Anat 188(Pt 1):1–15PubMedPubMedCentralGoogle Scholar
  16. MBF Bioscience (2011) Stereology information for the biological sciences. Accessed 21 Oct 2011
  17. Schmitz C, Hof PR (2005) Design-based stereology in neuroscience. Neuroscience 130:813–831CrossRefPubMedGoogle Scholar
  18. Sterio DC (1984) The unbiased estimation of number and sizes of arbitrary particles using the disector. J Microsc 134:127–136CrossRefPubMedGoogle Scholar
  19. Sundberg MD (1984) An Introduction to Stereolocial Analysis: Morphometric Techniques for Beginning Biologists, in Tested studies for laboratory teaching, S.E.A. Goldman CA, Hauta PL and Ketchum R, Editor 1984, Association for Biology Laboratory Education Memorial University of Newfoundland 51–72Google Scholar
  20. West MJ, Slomianka L, Gundersen HJ (1991) Unbiased stereological estimation of the total number of neurons in the subdivisions of the rat hippocampus using the optical fractionator. Anat Rec 231:482–497CrossRefPubMedGoogle Scholar
  21. Williams RW, Rakic P (1988) Three-dimensional counting: an accurate and direct method to estimate numbers of cells in sectioned material. J Comp Neurol 278:344–352CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Kristi M. Anderson
    • 1
  • Adam M. Szlachetka
    • 1
  • R. Lee Mosley
    • 1
    Email author
  1. 1.Department of Pharmacology and Experimental Neuroscience, Center for Neurodegenerative DisordersUniversity of Nebraska Medical CenterOmahaUSA

Personalised recommendations