Automated analysis of protein subcellular location in time series images

Learning Morphological, Spatial, and Dynamic Models of Cellular Components

In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.

A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images

Endosomes are subcellular organelles which serve as important transport compartments in eukaryotic cells. Fluorescence microscopy is a widely applied technology to study endosomes at the subcellular level. In general, a microscopy image can contain a large number of organelles and endosomes in particular. Detecting and annotating endosomes in fluorescence microscopy images is a critical part in the study of subcellular trafficking processes. Such annotation is usually performed by human inspection, which is time-consuming and prone to inaccuracy if carried out by inexperienced analysts. This paper proposes a two-stage method for automated detection of ring-like endosomes. The method consists of a localization stage cascaded by an identification stage. Given a test microscopy image, the localization stage generates a voting-map by locally comparing the query endosome patches and the test image based on a bag-of-words model. Using the voting-map, a number of candidate patches of endosomes are determined. Subsequently, in the identification stage, a support vector machine (SVM) is trained using the endosome patches and the background pattern patches. Each of the candidate patches is classified by the SVM to rule out those patches of endosome-like background patterns. The performance of the proposed method is evaluated with real microscopy images of human myeloid endothelial cells. It is shown that the proposed method significantly outperforms several state-of-the-art competing methods using multiple performance metrics.

Quantifying morphological heterogeneity: a study of more than 1 000 000 individual stored red blood cells

BACKGROUND AND OBJECTIVES

The morphology of red blood cells (RBCs) deteriorates progressively during hypothermic storage. The degree of deterioration varies between individual cells, resulting in a highly heterogeneous population of cells contained within each RBC unit. Current techniques capable of categorizing the morphology of individual stored RBCs are manual, laborious and error-prone procedures that limit the number of cells that can be studied. Our objective was to create a simple, automated system for high-throughput RBC morphology classification.

MATERIALS AND METHODS

A simple microfluidic device, designed to enable rapid, consistent acquisition of images of optimally oriented RBCs, was fabricated using soft lithography. A custom image analysis algorithm was developed to categorize the morphology of each individual RBC in the acquired images. The system was used to determine morphology of individual RBCs in several RBC units stored hypothermically for 6-8 weeks.

RESULTS

The system was used to automatically determine the distribution of cell diameter within each morphological class for >1 000 000 individual stored RBCs (speed: >10 000 cells/h; accuracy: 91·9% low resolution, 75·3% high resolution). Diameter mean and standard deviation by morphology class were as follows: discocyte 7·80 ± 0·49 μm, echinocyte 1 7·61 ± 0·63 μm, echinocyte 2 7·02 ± 0·61 μm, echinocyte 3 6·47 ± 0·42 μm, sphero-echinocyte 6·01 ± 0·26 μm, spherocyte 6·02 ± 0·27 μm, stomatocyte 1 6·95 ± 0·61 μm and stomatocyte 2 7·32 ± 0·47 μm.

CONCLUSIONS

The automated morphology classification procedure described in this study is significantly simpler, faster and less subjective than conventional manual procedures. The ability to evaluate the morphology of individual RBCs automatically, rapidly and in statistically significant numbers enabled us to perform the most extensive study of stored RBC morphology to date.

Learning a cost function for microscope image segmentation

Quantitative analysis of microscopy images is increasingly important in clinical researchers' efforts to unravel the cellular and molecular determinants of disease, and for pathological analysis of tissue samples. Yet, manual segmentation and measurement of cells or other features in images remains the norm in many fields. We report on a new system that aims for robust and accurate semi-automated analysis of microscope images. A user interactively outlines one or more examples of a target object in a training image. We then learn a cost function for detecting more objects of the same type, either in the same or different images. The cost function is incorporated into an active contour model, which can efficiently determine optimal boundaries by dynamic programming. We validate our approach and compare it to some standard alternatives on three different types of microscopic images: light microscopy of blood cells, light microscopy of muscle tissue sections, and electron microscopy cross-sections of axons and their myelin sheaths.

Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.

Model building and intelligent acquisition with application to protein subcellular location classification

MOTIVATION

We present a framework and algorithms to intelligently acquire movies of protein subcellular location patterns by learning their models as they are being acquired, and simultaneously determining how many cells to acquire as well as how many frames to acquire per cell. This is motivated by the desire to minimize acquisition time and photobleaching, given the need to build such models for all proteins, in all cell types, under all conditions. Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired.

RESULTS

We validate our framework on protein subcellular location classification, and show that the combination of model building and intelligent acquisition results in time and storage savings without loss of classification accuracy, or alternatively, higher classification accuracy for the same total acquisition time.

AVAILABILITY AND IMPLEMENTATION

The data and software used for this study will be made available upon publication at http://murphylab.web.cmu.edu/software and http://www.andrew.cmu.edu/user/jelenak/Software.

CONTACT

jelenak@cmu.edu.

The Plant Organelles Database 2 (PODB2): an updated resource containing movie data of plant organelle dynamics

The Plant Organelles Database (PODB) was launched in 2006 and provides imaging data of plant organelles, protocols for plant organelle research and external links to relevant websites. To provide comprehensive information on plant organelle dynamics and accommodate movie files that contain time-lapse images and 3D structure rotations, PODB was updated to the next version, PODB2 (http://podb.nibb.ac.jp/Organellome). PODB2 contains movie data submitted directly by plant researchers and can be freely downloaded. Through this organelle movie database, users can examine the dynamics of organelles of interest, including their movement, division, subcellular positioning and behavior, in response to external stimuli. In addition, the user interface for access and submission has been enhanced. PODB2 contains all of the information included in PODB, and the volume of data and protocols deposited in the PODB2 continues to grow steadily. Moreover, a new website, Plant Organelles World (http://podb.nibb.ac.jp/Organellome/PODBworld/en/index.html), which is based on PODB2, was recently launched as an educational tool to engage members of the non-scientific community such as students and school teachers. Plant Organelles World is written in layman's terms, and technical terms were avoided where possible. We would appreciate contributions of data from all plant researchers to enhance the usefulness of PODB2 and Plant Organelles World.