scholarly journals An end-to-end workflow for multiplexed image processing and analysis

2021 ◽  
Author(s):  
Jonas Windhager ◽  
Bernd Bodenmiller ◽  
Nils Eling
Keyword(s):  
Image Processing ◽  
Single Cell ◽  
Spatial Data ◽  
Single Cell Analysis ◽  
Relevant Information ◽  
Spatial Data Analysis ◽  
Imaging Data ◽  
Imaging Technologies ◽  
Biologically Relevant ◽  
Multiplexed Imaging

Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms. Here, we report the development of a user-friendly, customizable, and interoperable workflow for processing and analyzing data generated by highly multiplexed imaging technologies. The steinbock framework supports image pre-processing, segmentation, feature extraction, and standardized data export. Each step is performed in a reproducible fashion. The imcRtools R/Bioconductor package forms the bridge between image processing and single-cell analysis by directly importing data generated by steinbock. The package further supports spatial data analysis and integrates with tools developed within the Bioconductor project. Together, the tools described in this workflow facilitate analyses of multiplexed imaging raw data at the single-cell and spatial level.

scholarly journals A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution

2021 ◽  
Author(s):  
Michele Bortolomeazzi ◽  
Lucia Montorsi ◽  
Damjan Temelkovski ◽  
Mohamed Reda Keddar ◽  
Amelia Acha-Sagredo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Performance ◽  
Spatial Information ◽  
Single Cell Analysis ◽  
Biological Tissues ◽  
Imaging Data ◽  
Cell Identification ◽  
Spatially Resolved ◽  
Multiplexed Imaging

ABSTRACTMultiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at: https://github.com/ciccalab/SIMPLI.

scholarly journals cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

2020 ◽  
Author(s):  
Nils Eling ◽  
Nicolas Damond ◽  
Tobias Hoch ◽  
Bernd Bodenmiller
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging Data ◽  
Standard Data ◽  
Spatial Profiling ◽  
Bioconductor Project ◽  
Multiplexed Imaging ◽  
Level Information ◽  
Statistical Programming ◽  
Cell Phenotypes

SUMMARYHighly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data

Cell Systems ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1121-1123
Author(s):  
Inna Averbukh ◽  
Noah F. Greenwald ◽  
Candace C. Liu ◽  
Michael Angelo
Keyword(s):  
Single Cell ◽  
Imaging Data ◽  
Multiplexed Imaging

scholarly journals Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

2021 ◽  
Author(s):  
Coleman R Harris ◽  
Eliot T McKinley ◽  
Joseph T Roland ◽  
Qi Liu ◽  
Martha J Shrubsole ◽  
...  
Keyword(s):  
Functional Data ◽  
Single Cell Analysis ◽  
Evaluation Criteria ◽  
Evaluation Framework ◽  
Complex Data ◽  
Imaging Data ◽  
Data Registration ◽  
Multiplexed Imaging ◽  
In Situ Methods ◽  
Clear Slide

The multiplexed imaging domain is a nascent single-cell analysis field with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing this data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in the multiplexed domain.

scholarly journals Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

2021 ◽  
Vol 9 ◽  
Author(s):  
Cindy X. Chen ◽  
Han Sang Park ◽  
Hillel Price ◽  
Adam Wax
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Imaging Modality ◽  
Cell Types ◽  
Needle Aspiration ◽  
Microfluidic Channels ◽  
Phase Imaging ◽  
Imaging Data

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

scholarly journals Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology

2021 ◽  
Vol 8 ◽  
Author(s):  
Ryan P. Lau ◽  
Teresa H. Kim ◽  
Jianyu Rao
Keyword(s):  
Artificial Intelligence ◽  
Single Cell ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Image Cytometry ◽  
Needle Aspiration ◽  
Great Promise ◽  
Routine Practice ◽  
Imaging Data ◽  
Biomarker Analysis

Several advances in recent decades in digital imaging, artificial intelligence, and multiplex modalities have improved our ability to automatically analyze and interpret imaging data. Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity. Improvements in computer vision and machine learning have made algorithms more successful in automatically identifying important features to diagnose disease. Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration (FAST-FNA), and portable fluorescence-based image cytometry analyzer (CytoPAN) are under investigation. These have shown great promise in their ability to automatically analyze several biomarkers concurrently with high sensitivity, even in paucicellular samples, lending themselves well as tools in FNA. Not yet widely adopted for clinical use, many have successfully been applied to human samples. Once clinically validated, some of these technologies are poised to change the routine practice of cytopathology.

scholarly journals Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Grazing trajectory statistics and visualization platform based on cloud GIS

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Dong Li ◽  
Chuanjian Wang ◽  
Qilei Wang ◽  
Tianying Yan ◽  
Wanlong Bing ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Feed Intake ◽  
Spatial Data ◽  
Walking Speed ◽  
Relevant Information ◽  
Spatial Data Analysis ◽  
Related Field ◽  
Line Chart ◽  
Point Analysis ◽  
Field Information

Abstract It is very important for ranchers and grassland livestock management departments to master the information on the trajectory and feeding behavior of the herd timely and accurately. Therefore, this study developed a statistics and visualization platform for grazing trajectory. The platform was implemented by using the Web AppBuilder for ArcGIS framework and ArcGIS Online server. In particular, the trajectory processing service on the server was used to calculate walking speed, walking trajectory and feed intake of the herd in the platform. And these results were published to the ArcGIS Online server. The relevant information was analyzed and displayed by Web AppBuilder for ArcGIS calling the data on ArcGIS Online. Moreover, the paltform provided some visualization functions to support the visualization of user-defined analysis results. When users use the functions of spatial analysis (such as buffer analysis, finding hot pots analysis and interpolation point analysis), they can choose to analyze spatial data and related field information to conduct customized spatial data analysis. In a short, the platform realized the visualization functions of feed intake statistics, walking speed statistics, spatial analysis, line chart analysis and pie chart analysis of spatial data related attributes. It can provide technical support and data support for the relevant management departments to monitor grazing information and study the living habits of the herd.

scholarly journals A framework for multiplex imaging optimization and reproducible analysis

2021 ◽  
Author(s):  
Jennifer R Eng ◽  
Elmar Bucher ◽  
Zhi Hu ◽  
Ting Zheng ◽  
Summer Gibbs ◽  
...  
Keyword(s):  
Image Processing ◽  
Single Cell ◽  
Background Correction ◽  
Antibody Specificity ◽  
Imaging Technologies ◽  
Spatial Characterization ◽  
Batch Normalization ◽  
Reproducible Analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, jinxif, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

scholarly journals scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

2021 ◽  
Author(s):  
Anne Bertolini ◽  
Michael Prummer ◽  
Mustafa Anil Tuncel ◽  
Ulrike Menzel ◽  
María Lourdes Rosano-González ◽  
...  
Keyword(s):  
Single Cell ◽  
Clinical Decision Making ◽  
Single Cell Analysis ◽  
Clinical Decision ◽  
Relevant Information ◽  
Tumor Board ◽  
Rna Seq ◽  
Candidate Drug ◽  
Sample Composition ◽  
Drug Treatments

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

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