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高精度二维投影估计颗粒大小和密度的体视学法验证。

Validation of a stereological method for estimating particle size and density from 2D projections with high accuracy.

机构信息

Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.

Cellular Neuroscience, Institute of Science and Technology Austria, Klosterneuburg, Austria.

出版信息

PLoS One. 2023 Mar 17;18(3):e0277148. doi: 10.1371/journal.pone.0277148. eCollection 2023.

Abstract

Stereological methods for estimating the 3D particle size and density from 2D projections are essential to many research fields. These methods are, however, prone to errors arising from undetected particle profiles due to sectioning and limited resolution, known as 'lost caps'. A potential solution developed by Keiding, Jensen, and Ranek in 1972, which we refer to as the Keiding model, accounts for lost caps by quantifying the smallest detectable profile in terms of its limiting 'cap angle' (ϕ), a size-independent measure of a particle's distance from the section surface. However, this simple solution has not been widely adopted nor tested. Rather, model-independent design-based stereological methods, which do not explicitly account for lost caps, have come to the fore. Here, we provide the first experimental validation of the Keiding model by comparing the size and density of particles estimated from 2D projections with direct measurement from 3D EM reconstructions of the same tissue. We applied the Keiding model to estimate the size and density of somata, nuclei and vesicles in the cerebellum of mice and rats, where high packing density can be problematic for design-based methods. Our analysis reveals a Gaussian distribution for ϕ rather than a single value. Nevertheless, curve fits of the Keiding model to the 2D diameter distribution accurately estimate the mean ϕ and 3D diameter distribution. While systematic testing using simulations revealed an upper limit to determining ϕ, our analysis shows that estimated ϕ can be used to determine the 3D particle density from the 2D density under a wide range of conditions, and this method is potentially more accurate than minimum-size-based lost-cap corrections and disector methods. Our results show the Keiding model provides an efficient means of accurately estimating the size and density of particles from 2D projections even under conditions of a high density.

摘要

体视学方法可通过二维投影来估计三维颗粒的大小和密度,这对许多研究领域都至关重要。然而,这些方法容易受到由于切片和分辨率有限而导致的未检测到颗粒轮廓的影响,这些未检测到的颗粒轮廓被称为“丢失的帽”。1972 年,Keiding、Jensen 和 Ranek 开发了一种潜在的解决方案,我们称之为 Keiding 模型,该模型通过量化最小可检测轮廓的极限“帽角”(ϕ)来解释丢失的帽,这是一个与颗粒距离切片表面的大小无关的测量值。然而,这种简单的解决方案并没有被广泛采用或测试。相反,不明确考虑丢失的帽的基于模型的设计立体学方法已经成为主流。在这里,我们通过将从二维投影估计的颗粒大小和密度与同一组织的三维 EM 重建的直接测量进行比较,首次对 Keiding 模型进行了实验验证。我们将 Keiding 模型应用于估计小鼠和大鼠小脑的胞体、核和囊泡的大小和密度,其中高堆积密度对基于设计的方法来说是个问题。我们的分析揭示了 ϕ 的高斯分布,而不是单个值。尽管如此,对 Keiding 模型对二维直径分布的曲线拟合可以准确估计平均ϕ 和三维直径分布。虽然使用模拟进行系统测试揭示了确定ϕ 的上限,但我们的分析表明,在广泛的条件下,可以使用估计的ϕ 从二维密度确定三维颗粒密度,并且该方法比基于最小尺寸的丢失帽校正和切割器方法更准确。我们的结果表明,即使在高密度条件下,Keiding 模型也可以提供一种从二维投影准确估计颗粒大小和密度的有效方法。

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