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

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.

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

10.1101/2021.04.01.437886 ◽

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.

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Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data

Frontiers in Immunology ◽

10.3389/fimmu.2021.727626 ◽

2021 ◽

Vol 12 ◽

Author(s):

John W. Hickey ◽

Yuqi Tan ◽

Garry P. Nolan ◽

Yury Goltsev

Keyword(s):

Single Cell ◽

Cell Biology ◽

Clustering Algorithm ◽

Cell Types ◽

Unsupervised Clustering ◽

Cell Segmentation ◽

Imaging Data ◽

Cell Type ◽

Multiplexed Imaging ◽

Four Levels

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.

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Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

10.1101/2021.06.05.447183 ◽

2021 ◽

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 cells composition and interaction in tumor microenvironment 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 40 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 for 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-XMBD, 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 in IMC, 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.

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

10.1101/2021.11.12.468357 ◽

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.

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

10.1101/2020.09.08.287516 ◽

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.

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E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

10.1101/080622 ◽

2016 ◽

Author(s):

Maia A. Smith ◽

Cydney Nielsen ◽

Fong Chun Chan ◽

Andrew McPherson ◽

Andrew Roth ◽

...

Keyword(s):

Single Cell ◽

Visual Analytics ◽

Domain Knowledge ◽

Treatment Resistance ◽

Cancer Evolution ◽

Imaging Data ◽

Web Based ◽

Clinical Endpoints ◽

Cell Experiment ◽

Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

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

10.1101/2021.07.16.452359 ◽

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.

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Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

Cell Systems ◽

10.1016/j.cels.2017.10.012 ◽

2018 ◽

Vol 6 (1) ◽

pp. 37-51.e9 ◽

Cited By ~ 74

Author(s):

Charles A. Herring ◽

Amrita Banerjee ◽

Eliot T. McKinley ◽

Alan J. Simmons ◽

Jie Ping ◽

...

Keyword(s):

Single Cell ◽

Trajectory Analysis ◽

Rna Seq ◽

Imaging Data ◽

Tuft Cell

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

Frontiers in Physics ◽

10.3389/fphy.2021.759142 ◽

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.

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