Calibration of the stereological estimation of the number of myelinated axons in the rat sciatic nerve: A multicenter study


Kaplan S., GEUNA S., Ronchi G., Ulkay M. B. , von Bartheld C. S.

JOURNAL OF NEUROSCIENCE METHODS, cilt.187, sa.1, ss.90-99, 2010 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 187 Konu: 1
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.jneumeth.2010.01.001
  • Dergi Adı: JOURNAL OF NEUROSCIENCE METHODS
  • Sayfa Sayıları: ss.90-99

Özet

Several sources of variability can affect stereological estimates. Here we measured the impact of potential sources of variability on numerical stereological estimates of myelinated axons in the adult rat sciatic nerve. Besides biological variation, parameters tested included two variations of stereological methods (unbiased counting frame versus 2D-disector), two sampling schemes (few large versus frequent small sampling boxes), and workstations with varying degrees of sophistication. All estimates were validated against exhaustive counts of the same nerve cross sections to obtain calibrated true numbers of myelinated axons (gold standard). In addition, we quantified errors in particle identification by comparing light microscopic and electron microscopic images of selected consecutive sections. Biological variation was 15.6%. There was no significant difference between the two stereological approaches or workstations used, but sampling schemes with few large samples yielded larger differences (20.7 +/- 3.7% SEM) of estimates from true values, while frequent small samples showed significantly smaller differences (12.7 +/- 1.9% SEM). Particle identification was accurate in 94% of cases (range: 89-98%). The most common identification error was due to profiles of Schwann cell nuclei mimicking profiles of small myelinated nerve fibers. We recommend sampling frequent small rather than few large areas, and conclude that workstations with basic stereological equipment are sufficient to obtain accurate estimates. Electron microscopic verification showed that particle misidentification had a surprisingly variable and large impact of up to 11%, corresponding to 2/3 of the biological variation (15.6%). Thus, errors in particle identification require further attention, and we provide a simple nerve fiber recognition test to assist investigators with self-testing and training. (C) 2010 Elsevier B.V. All rights reserved.

Several sources of variability can affect stereological estimates. Here we measured the impact of potential sources of variability on numerical stereological estimates of myelinated axons in the adult rat sciatic nerve. Besides biological variation, parameters tested included two variations of stereological methods (unbiased counting frame versus 2Ddisector), two sampling schemes (few large versus frequent small sampling boxes), and workstations with varying degrees of sophistication. All estimates were validated against exhaustive counts of the same nerve cross sections to obtain calibrated true numbers of myelinated axons (gold standard). In addition, we quantified errors in particle identification by comparing light microscopic and electron microscopic images of selected consecutive sections. Biological variation was 15.6%. There was no significant difference between the two stereological approaches or workstations used, but sampling schemes with few large samples yielded larger differences (20.7 +3.7% SEM) of estimates from true values, while frequent small samples showed significantly smaller differences  (12.7 +1.9% SEM). Particle identification was accurate in 94% of cases (range: 89–98%). The most common identification error was due to profiles of Schwann cell nuclei mimicking profiles of small myelinated nerve fibers. We recommend sampling frequent small rather than few large areas, and conclude that workstations with basic stereological equipment are sufficient to obtain accurate estimates. Electron microscopic verification showed that particle misidentification had a surprisingly variable and large impact of up to 11%, corresponding to 2/3 of the biological variation (15.6%). Thus, errors in particle identification require further attention, and we provide a simple nerve fiber recognition test to assist investigators with selftesting and training.