A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images

Abstract

The microtubule network plays critical roles in many cellular processes, and quantitative models of how its organization varies across cell types and conditions are required for understanding those roles and as input to cell simulations. High-throughput image acquisition technologies are potentially valuable for this purpose, but do not provide sufficient resolution for current analysis methods that rely on tracing of individual microtubules. We describe a parametric conditional model of microtubule distribution that can generate a microtubule network in intact cells using a persistent random walk approach. The model parameters are physically meaningful as they directly describe the spatial distribution of microtubules and include the number of microtubules as well as the mean of the length distribution. We also present an indirect method for estimating the parameters of the model from three-dimensional fluorescence microscope images of cells that relies on comparing acquired images with simulated images generated from the model. Our results show that our method can reasonably recover parameters for a given query image, and we present the distributions of parameters estimated by our method for a collection of HeLa cell images. ' 2010 International Society for Advancement of Cytometry Key terms microtubule networks; parameter estimation; fluorescence microscopy; inverse modeling; cell simulation UNDERSTANDING the many complex cellular and subcellular processes underlying biological phenomena will require approaches for obtaining spatiotemporal information for the many thousands of proteins expressed in a typical cell. These measurements (in many cases in the form of statistical estimates) can then be used in modeling and simulation efforts where the goal is to predict and help understand cellular systems. One such example in microtubule organization is the simulation of microtubules with motor proteins in order to understand their phenotypic behavior (1). Another example in the area of cytoskeleton organization is the simulation of actin to understand the lamellipodial behavior of a cell (2).Among the many relevant cellular phenomena to be modeled and quantified, the subcellular spatial distribution of proteins (their location and overall organization) is important because of the crucial role that location plays in many cellular phenomena. Many neurodegenerative diseases such as Alzheimer's and Parkinson's are related to the malfunction of microtubule associated proteins and the microtubule network that leads to the accumulation of protein aggregates in brain cells (3). Technologies that facilitate the quantitative analysis of protein location, on a proteomewide scale, therefore would have potentially high impact. Many approaches have been described for obtaining subcellular location data of large numbers of protein distributions (4-8). Green fluorescent protein (GFP) tagging has emerged as the most widely used tool for this purpose and has enabled pro-

Extracting microtubule networks from superresolution single-molecule localization microscopy data

“…, 2014), sparse filaments from in vitro experiments (Xu et al. , 2014), or representative models of filament traces (Shariff et al. , 2010; Wood et al.…”

Image‐derived, three‐dimensional generative models of cellular organization

“…In particular, methods are needed to capture patterns not well represented by discrete objects. Work on learning generative models for microtubule distributions represents an initial step in this direction [16]. …”

CellOrganizer: Image-Derived Models of Subcellular Organization and Protein Distribution

“…This approach has been used to study kinetochore-microtubule dynamics (18). We have used a similar approach to build a generative model of microtubules in interphase HeLa cells and 3T3 cells (9, 10). An example of a synthetic microtubule distribution is shown in Fig.…”