The Automatic Proportionator Estimator Is Highly Efficient for Estimation of Total Number of Sparse Cell Populations

Abstract

Estimation of total number of a population of cells that are sparsely distributed in an organ or anatomically-defined region of interest represents a challenge for conventional stereological methods. In these situations, classic fractionator approaches that rely on systematic uniform random sampling are highly inefficient and, in many cases, impractical due to the intense sampling of the organ and tissue sections that is required to obtain sufficient counts for an acceptable level of precision. The proportionator, an estimator based on non-uniform sampling theory, marries automated image analysis with stereological principles and is the only estimator that provides a highly efficient and precise method to address these challenging quantification problems. In this paper, the practical considerations of the proportionator estimator and its implementation with Proportionator™ software and digital slide imaging are reviewed. The power of the proportionator as a stereological tool is illustrated in its application to the estimation of the total number of a very rare (~50/vertebrae) and sparsely distributed population of osteoprogenitor cells in mouse vertebral body. The proportionator offers a solution to neuroscientists interested in quantifying total cell number of sparse cell populations in the central and peripheral nervous system where systematic uniform random sampling-based stereological estimators are impractical.

Estimation of absolute number of alveolar epithelial type 2 cells in mouse lungs: a comparison between stereology and flow cytometry

“…Attempts to automate three-dimensional counting methods have met with dismal results, largely due to lower detection rates and higher false-positive rates (Schmitz et al, 2014). However, efforts remain underway to improve efficiency in stereology, such as the recently introduced application of the proportionator and the autodisector using virtual slides (Keller et al, 2013), and the application of the proportionator to the study of total cell numbers in sparse cell populations (Boyce & Gundersen, 2018).…”

QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain

“…A known challenge for the stereological evaluation was the sparse distribution of the plaques throughout the brain, and the concentration of the plaques in the frontal regions, which could at least partially account for the discrepancy between the stereological outputs and the outputs from the manual object delineations. As suggested by Boyce and Gundersen (Boyce and Gundersen, 2018), the classic fractionator approaches that rely on systematic random sampling are highly inefficient and impractical for sparse labelling. However, by increasing the sampling frequency in our stereological analysis, we obtained results very close to the manual delineation of objects.…”

Age-dependent changes in the medial prefrontal cortex and medial amygdala structure, and elevated plus-maze performance in the healthy male Wistar rats

“…Fifty μm-thick coronal sections of the brains were cut by cryostat (Leica, CM1860, Germany) and stored in a cryoprotectant solution at −20 °C until use for Cresyl Violet staining. An average of ten sections per animal were selected using uniform systematic random sampling and stained with Cresyl Violet for estimating the volume and neuron or non-neuron counting, and spatial distribution of the neurons in the mPFC (AP = 4.20 to -1.50) or MeA(-1.44 to -3.60) regions based on the rat brain atlas ( Boyce and Gundersen, 2018 ; Paxinos and Watson, 2006 ).…”

“…Numerical density and the total number of cells (neuron or non-neuron) in the mPFC and MeA were estimated, using the optical disector method ( Boyce and Gundersen, 2018 ). This technique eliminates bias in counting as a result of cell size or shape.…”