Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

Geuenich, Michael; Hou, Jianquan; Lee, Sunyun; Ayub, Shanza; Jackson, Hartland W.; Campbell, Kieran R

doi:10.1016/j.cels.2021.08.012

Cited by 50 publications

(62 citation statements)

References 44 publications

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“…While this procedure may be laborious, it enables the detection of novel cell types and states. Nonetheless, methods for automated prediction of cell type identities such as Astir and Stellar also exist [ 98 , 99 ]. Single-cells and their phenotypes can be visualized either by projecting feature intensity as colors on scatter plots of reduced dimensionality projections, or by visualizing features on heatmaps at the single-cell or cell type level.…”

Section: Computational Methodologies To Analyze Spatial Omics Datamentioning

confidence: 99%

Spatial omics technologies at multimodal and single cell/subcellular level

et al. 2022

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Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

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Section: Computational Methodologies To Analyze Spatial Omics Datamentioning

confidence: 99%

Spatial omics technologies at multimodal and single cell/subcellular level

et al. 2022

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“…Astir is a scalable, probabilistic method that uses deep recognition neural networks and input from the operator in the form of predefined knowledge of marker expression patterns in specific, a priori known and expected cell classes to perform cell phenotyping in the supervised, hierarchical manner [ https://www.github.com/camlab-bioml/astir , ( Geuenich et al , 2021 )]. The main statistical model used in Astir relies on the assumption that the cell phenotype is a static and unchanged pattern of expression of certain markers and lack of expression of others.…”

Section: Downstream Analysismentioning

confidence: 99%

Different approaches to Imaging Mass Cytometry data analysis

Milošević¹

2023

Bioinformatics Advances

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Imaging Mass Cytometry (IMC) is a novel, high multiplexing imaging platform capable of simultaneously detecting and visualizing up to 40 different protein targets. It is a strong asset available for in-depth study of histology and pathophysiology of the tissues. Bearing in mind the robustness of this technique and the high spatial context of the data it gives, it is especially valuable in studying the biology of cancer and tumor microenvironment. IMC-derived data are not classical micrographic images, and due to the characteristics of the data obtained using IMC, the image analysis approach, in this case, can diverge to a certain degree from the classical image analysis pipelines. As the number of publications based on the IMC is on the rise, this trend is also followed by an increase in the number of available methodologies designated solely to IMC-derived data analysis. This review has for an aim to give a systematic synopsis of all the available classical image analysis tools and pipelines useful to be employed for IMC data analysis and give an overview of tools intentionally developed solely for this purpose, easing the choice to researchers of selecting the most suitable methodologies for a specific type of analysis desired.

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“…Over the past 5 years, various computational methods and software tools have been developed to facilitate each of these analytical steps. Most carry out only one of the steps (CODEX Uploader [ 5 ], RAPID [ 25 ], CellSeg [ 26 ], Mesmer [ 27 ], CellPose [ 28 ], CELESTA [ 29 ], Astir [ 30 ], CytoMAP [ 31 ], histoCAT [ 32 ], TissueSchematics [ 33 ], and MISTy [ 34 ]), but a few attempt to address multiple steps or the entire workflow (Cytokit [ 35 ], MCMICRO [ 36 ], SIMPLI [ 37 ]) (Table 2 ).…”

Section: Bioinformatic Analysismentioning

confidence: 99%

“…Although efforts have been made to develop new algorithms for automatic and fast cell type identification, robust and accurate algorithms that can detect cells of various abundances from multiplexed imaging data are still lacking. Astir [ 30 ] is a probabilistic machine learning method that infers cell types based on protein expression of cells and prior knowledge of marker proteins. This algorithm has superior accuracy and runtime compared to unsupervised clustering methods in mass cytometry data.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Highly multiplexed spatial profiling with CODEX: bioinformatic analysis and application in human disease

Kuswanto

Nolan

2022

Semin Immunopathol

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Multiplexed imaging, which enables spatial localization of proteins and RNA to cells within tissues, complements existing multi-omic technologies and has deepened our understanding of health and disease. CODEX, a multiplexed single-cell imaging technology, utilizes a microfluidics system that incorporates DNA barcoded antibodies to visualize 50 + cellular markers at the single-cell level. Here, we discuss the latest applications of CODEX to studies of cancer, autoimmunity, and infection as well as current bioinformatics approaches for analysis of multiplexed imaging data from preprocessing to cell segmentation and marker quantification to spatial analysis techniques. We conclude with a commentary on the challenges and future developments for multiplexed spatial profiling.

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Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

Abstract: Highlights d Automatically assign cell types based on an expression matrix and a marker file d Astir is robust to imbalances in cell type compositions and cell segmentations d Output probabilities are interpretable and correlate with image staining quality

Cited by 50 publications

References 44 publications

Spatial omics technologies at multimodal and single cell/subcellular level

Spatial omics technologies at multimodal and single cell/subcellular level

Different approaches to Imaging Mass Cytometry data analysis

Highly multiplexed spatial profiling with CODEX: bioinformatic analysis and application in human disease

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