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Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第4期62卷 1105-1119页

作者： Bao, Lin-Xia Luo, Zhuo-Ming Zhu, Xi-Liang Xu, Ying-Ying Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Imaging Proc Guangzhou 510515 Peoples R China Southern Med Univ Guangdong Prov Engn Lab Med Imaging & Diagnost Tec Guangzhou 510623 Peoples R China

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

关键词： bioimage informatics Mouse brain Protein expression Immunofluorescence image Cellular location

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pyHiM: a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging

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GENOME BIOLOGY 2024年第1期25卷 1页

作者： Devos, Xavier Fiche, Jean-Bernard Bardou, Marion Messina, Olivier Houbron, Christophe Gurgo, Julian Schaeffer, Marie Gotz, Markus Walter, Thomas Mueller, Florian Nollmann, Marcelo Univ Montpellier Ctr Biol Struct CNRS INSERMU1054UMR 5048 F-34090 Montpellier France PSL Univ Ctr Computat Biol CBIO Mines Paris F-75006 Paris France Inst Curie F-75248 Paris France INSERM U900 F-75248 Paris France Univ Paris Cite Inst Pasteur Imaging & Modeling Unit Paris France

Genome-wide ensemble sequencing methods improved our understanding of chromatin organization in eukaryotes but lack the ability to capture single-cell heterogeneity and spatial organization. To overcome these limitations, new imaging-based methods have emerged, giving rise to the field of spatial genomics. Here, we present pyHiM, a user-friendly python toolbox specifically designed for the analysis of multiplexed DNA-FISH data and the reconstruction of chromatin traces in individual cells. pyHiM employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis.

关键词： Spatial genomics 3D chromatin structure Transcription Imaging bioimage informatics

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Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning

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BRIEFINGS IN BIOinformatics 2023年第1期24卷 bbac609-bbac609页

作者： Zhu, Xi-Liang Bao, Lin-Xia Xue, Min-Qi Xu, Ying-Ying Southern Med Univ Guangzhou Peoples R China Southern Med Univ Sch Biomed Engn Guangzhou Peoples R China

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

关键词： bioimage informatics multi-instance learning deep learning protein subcellular localization

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Automated Image Analysis for High-Content Screening and Analysis

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JOURNAL OF BIOMOLECULAR SCREENING 2010年第7期15卷 726-734页

作者： Shariff, Aabid Kangas, Joshua Coelho, Luis Pedro Quinn, Shannon Murphy, Robert F. Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Joint Carnegie Mellon Univ Univ Pittsburgh Ph D P Pittsburgh PA USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Univ Freiburg Freiburg Inst Adv Studies Freiburg Germany

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications. (Journal of Biomolecular Screening 2010:726-734)

关键词： image analysis bioimage informatics high-content screening machine learning subcellular location

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An automated quantitative image analysis tool for the identification of microtubule patterns in plants

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TRAFFIC 2017年第10期18卷 683-693页

作者： Faulkner, Christine Zhou, Ji Evrard, Alexandre Bourdais, Gildas MacLean, Dan Haweker, Heidrun Eckes, Peter Robatzek, Silke Norwich Res Pk Sainsbury Lab Norwich Norfolk England Bayer AG Crop Sci Div Ind Pk Hoechst Frankfurt Germany Norwich Res Pk John Innes Ctr Norwich Norfolk England Norwich Res Pk Earlham Inst Norwich Norfolk England

High throughput confocal imaging poses challenges in the computational image analysis of complex subcellular structures such as the microtubule cytoskeleton. Here, we developed CellArchitect, an automated image analysis tool that quantifies changes to subcellular patterns illustrated by microtubule markers in plants. We screened microtubule-targeted herbicides and demonstrate that high throughput confocal imaging with integrated image analysis by CellArchitect can distinguish effects induced by the known herbicides indaziflam and trifluralin. The same platform was used to examine 6 other compounds with herbicidal activity, and at least 3 different effects induced by these compounds were profiled. We further show that CellArchitect can detect subcellular patterns tagged by actin and endoplasmic reticulum markers. Thus, the platform developed here can be used to automate image analysis of complex subcellular patterns for purposes such as herbicide discovery and mode of action characterisation. The capacity to use this tool to quantitatively characterize cellular responses lends itself to application across many areas of biology.

关键词： Arabidopsis bioimage informatics confocal microscopy endomembrane compartments herbicides MAP4 microtubules TUB6

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On comparing heterogeneity across biomarkers

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CYTOMETRY PART A 2015年第6期87A卷 558-567页

作者： Steininger, Robert J., III Rajaram, Satwik Girard, Luc Minna, John D. Wu, Lani F. Altschuler, Steven J. Univ Texas SW Med Ctr Dallas Green Ctr Syst Biol Dallas TX 75390 USA Univ Calif San Francisco Dept Pharmaceut Chem San Francisco CA 94143 USA Univ Texas SW Med Ctr Dallas Simmons Comprehens Canc Ctr Hamon Ctr Therapeut Oncol Res Dallas TX 75390 USA Univ Texas SW Med Ctr Dallas Dept Pharmacol & Inter Med Dallas TX 75390 USA

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells;instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the gold standard of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems. (c) 2014 International Society for Advancement of Cytometry

关键词： heterogeneity biomarker selection systems biology microscopy bioimage informatics biological networks single-cell variability information theory

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Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysis

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JOURNAL OF MICROSCOPY 2011年第3期243卷 284-292页

作者： Shamir, L. Lawrence Technol Univ Dept Comp Sci Southfield MI USA

The increasing prevalence of automated image acquisition systems and state-of-the-art information technology has enabled new types of microscopy experiments based on automatic processing of massive image data sets, and numerous methods of high-content screening using machine vision and pattern recognition methods have been proposed. However, as a relatively young discipline, it is important to validate these methods and ensure that the machine vision and pattern recognition techniques reliably reflect the actual morphology, and can be effectively used for finding and validating scientific discoveries. In this report we show that some of the previously reported experimental results using automatic microscopy image analysis might be biased, and discuss practices and methods that can be used to obtain objective and reliable automatic analysis of microscopy images.

关键词： bioimage informatics image analysis quantitative microscopy

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A novel method for quantified, superresolved, three-dimensional colocalisation of isotropic, fluorescent particles

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HISTOCHEMISTRY AND CELL BIOLOGY 2013年第3期139卷 391-402页

作者： Obara, Boguslaw Jabeen, Asma Fernandez, Nelson Laissue, Pierre Philippe Univ Durham Sch Engn & Comp Sci Durham DH1 3LE England Univ Oxford Oxford Ctr Integrat Syst Biol Oxford OX1 3QG England Univ Oxford Oxford E Res Ctr Oxford OX1 3QG England Univ Essex Sch Biol Sci Colchester CO4 3SQ Essex England

Colocalisation, the overlap of subcellular structures labelled with different colours, is a key step to characterise cellular phenotypes. We have developed a novel bioimage informatics approach for quantifying colocalisation of round, blob-like structures in two-colour, highly resolved, three-dimensional fluorescence microscopy datasets. First, the algorithm identifies isotropic fluorescent particles, of relative brightness compared to their immediate neighbourhood, in three dimensions and for each colour. The centroids of these spots are then determined, and each object in one location of a colour image is checked for a corresponding object in the other colour image. Three-dimensional distance maps between the centroids of differently coloured spots then display where and how closely they colocalise, while histograms allow to analyse all colocalisation distances. We use the method to reveal sparse colocalisation of different human leukocyte antigen receptors in choriocarcinoma cells. It can also be applied to other isotropic subcellular structures such as vesicles, aggresomes and chloroplasts. The simple, robust and fast approach yields superresolved, object-based colocalisation maps and provides a first indication of protein-protein interactions of fluorescent, isotropic particles.

关键词： Colocalisation Quantification Superresolution Confocal microscopy Surface receptors bioimage informatics

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Computerized cell tracking:Current methods,tools and challenges

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Visual informatics 2021年第1期5卷 1-13页

作者： Neda Emami Zahra Sedaei Reza Ferdousi Department of Health Information Technology School of Management and Medical InformaticsTabriz University of Medical SciencesTabriz5166/15731Iran Department of Health Services Management School of Management and Medical InformaticsTabriz University of Medical SciencesTabriz5166/15731Iran

In developmental biology,knowledge of cell structure and their(morpho)dynamic behavior,leads to a comprehensive understanding of their conducts and the mechanisms in which they *** knowledge is a decisive factor in biological research and also in all drug development steps,medicinal or preventive *** cell analysis is hard,expensive,and *** overcome these difficulties,in recent years,several computational object tracking methods,software system and packages have been developed in cell sciences that bring together different disciplines and branches of *** tracking is the process of locating and monitoring specific object and its behavior in sequential *** this paper,a comprehensive review on object tracking stages and computational methods that are utilized in terms of cell tracking has been ***,the available software packages and toolkits,challenges,and their solution in time lapse microscopy images in this scope were *** aim of describing computational cell tracking methods and tools is that biologist and cell scientists might take advantage of these computational techniques to find another method to gain complementary information for their question of interest.

关键词： Cell tracking Digital cell tracking Live-cell microscopy Image analysis bioimage informatics Subcellular location

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Automatic Cell Identification in the Unique System of Invariant Embryogenesis in Caenorhabditis elegans

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BIOMEDICAL ENGINEERING LETTERS 2014年第4期4卷 328-337页

作者： Azuma, Yusuke Onami, Shuichi RIKEN Quantitat Biol Ctr Lab Dev Dynam Kobe Hyogo 6500047 Japan Japan Sci & Technol Agcy Natl Biosci Database Ctr Tokyo 1020081 Japan

In recent years, development of image processing methods has rapidly progressed to automatically identify spatiotemporal dynamics of embryonic cells in Caenorhabditis elegans. The methods allow quantitative and high-throughput analysis for molecular dynamics during embryogenesis. In turn, the analyzed dynamics can be merged onto a reference embryo, providing an integrated view of embryogenesis. This integration is enabled by invariant embryogenesis of C. elegans, which is the most unique advantage offered by this organism. Therefore, the key point in the development of the methods is how to take advantage of this feature. In this article, we review a series of development of such methods and their applications. First, we describe basic image processing methods that are the basis for development of cell identification methods. Next, we describe methods that have succeeded to identify cells in images and their performance. Finally, we review studies that have employed cell identification methods to analyze the variability of cellular dynamics, cell-cell contacts and cell fate determination. Together with advances in imaging technologies to measure molecular dynamics and computational methods to identify such dynamics with high accuracy, the unique system of invariant embryogenesis in C. elegans will be invaluable to study developmental mechanisms. Therefore, it is important to understand the ever developing technologies and their results.

关键词： bioimage informatics Image processing Cell tracking Caenorhabditis elegans

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