Cross-platform dataset of multiplex fluorescent cellular object image annotations

Aleynick, Nathaniel; Li, Yanyun; Xie, Yubin; Zhang, Mianlei; Posner, Andrew; Roshal, Lev; Pe’er, Dana; Vanguri, R.; Hollmann, Travis J.

doi:10.1038/s41597-023-02108-z

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Furthermore, we also benchmarked segmentation performance of UNSEG with respect to Cellpose and Mesmer using publicly available, multiplexed imaging tissue datasets acquired using CODEX, Vectra, and Zeiss imaging platforms 36 . These datasets include cell annotations.…”

Section: Unseg Benchmarkingmentioning

confidence: 99%

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UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Kochetov,

Bell,

Garcia

et al. 2023

Preprint

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Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct ana posterioriprobability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue samples than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an example biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and accurately segmenting a cell nuclei cluster into individual cell nuclei. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to replace classical watershed. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue dataset, which we anticipate will be useful for the broader research community. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

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Section: Unseg Benchmarkingmentioning

confidence: 99%

“…Similarly, Supplementary Figures 5 and 6 compare the performance of UNSEG cell segmentation with that of Cellpose and Mesmer for Vectra and Zeiss datasets 36 respectively. The Vectra dataset includes 131 images of size 400 × 400, while the Zeiss dataset consists of nineteen images of size 800 × 800.…”

Section: Unseg Benchmarkingmentioning

confidence: 99%

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Kochetov,

Bell,

Garcia

et al. 2023

Preprint

View full text Add to dashboard Cite

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Cross-platform dataset of multiplex fluorescent cellular object image annotations

Aleynick

Xie

et al. 2023

Sci Data

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Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community.

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Get to know your neighbors with a SNAQ^TM: A framework for single cell spatial neighborhood analysis in immunohistochemical images

Silver,

Chakraborty,

Pittu

et al. 2024

Preprint

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MotivationAnalyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology.ResultsWe introduce an R Markdown script called SNAQTM(Single-cell SpatialNeighborhoodAnalysis andQuantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQTMwas used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQTM. Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest.Availability and implementationThe R Markdown files that comprise the SNAQTMalgorithm and the input data from this paper are freely available on the web athttps://github.com/AryehSilver1/SNAQ.Visual AbstractCreated with BioRender.com.

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Cross-platform dataset of multiplex fluorescent cellular object image annotations

Cited by 3 publications

References 12 publications

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Cross-platform dataset of multiplex fluorescent cellular object image annotations

Get to know your neighbors with a SNAQ^TM: A framework for single cell spatial neighborhood analysis in immunohistochemical images

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Cross-platform dataset of multiplex fluorescent cellular object image annotations

Cited by 3 publications

References 12 publications

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Cross-platform dataset of multiplex fluorescent cellular object image annotations

Get to know your neighbors with a SNAQTM: A framework for single cell spatial neighborhood analysis in immunohistochemical images

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Product

Resources

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Get to know your neighbors with a SNAQ^TM: A framework for single cell spatial neighborhood analysis in immunohistochemical images