Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics

Su, Pin-Rui; Li, You; Beerens, Cecile; Bezstarosti, Karel; Demmers, Jeroen; Pabst, Martin; Kanaar, Roland; Hsu, Cheng-Chih; Chien, Miao‐Ping

doi:10.1016/j.crmeth.2022.100237

Cited by 11 publications

(17 citation statements)

References 25 publications

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“…When combined with high-throughput screening microscopy, FACT can be used to instantly identify rare subpopulations of cells during large scale image data acquisition, enabling immediate isolation of target cells for downstream assays, like single cell sequencing or proteomic profiling 3,13 . For example, FACT can instantly identify sparse, tripolar dividing cells, aggressively migrating GBM cells, and fast dividing cells, all extremely low occurrence rate events.…”

Section: Fact Identifies Abnormal Cancer Cellsmentioning

confidence: 99%

“…The capability to rapidly process and isolate subpopulations of cells from a large pool of heterogenous cells opens an unprecedented possibility to dissect molecular mechanisms of phenotypes of interest, as the isolated cells with desired phenotypes can be directly subjected to downstream (single cell) sequencing and profiling. The profiled genomes, transcriptomes or proteomes can be straightforwardly linked to their corresponding cellular phenotypes (namely, genotype-to-phenotype linking) 3,12,13 . Despite the remarkable advances in fast cell tracking methods (cell segmentation + cell linking), these methods decrease tracking accuracy when cells have complicated cellular behaviors (like cells migrate across each other) or cells are densely populated (> ~100,000 cells/cm 2 ); such a density often occurs when tracking cells or studying cell lineages over days or is required when searching for rare biological events (0-5 %).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…With this processing speed, we can, for example, identify extremely rare tripolar dividing cells: ∼50 division events, resulting in 3 cells per division for a total of ∼150 cells, from a population of ∼0.8 million MCF10A breast cancer cells (∼0.05-0.1% occurrence rate) in ∼5 minutes after data acquisition (∼100 seconds of cell segmentation + ∼200 seconds of tripolar division detection) (Figure S7). Identifying larger than ∼100 cells of interest (from a cell population) is essential for experiments involving downstream single cell isolation and sequencing 3,13 , and profiling > ∼100 cells is required to draw a statistical conclusion: this makes it necessary to screen, segment and identify individual cells in a population of close to a million cells in a matter of minutes which GTWS makes possible.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Chou

Beerens

et al. 2023

Preprint

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Quantitative characterizations of cellular dynamics and features of individual cells from a large heterogenous population is essential to identify rare, disease-driving cells, which often exhibit aberrant cellular behaviors like abnormal division, aggressive migration or irregular phylogenetic cell lineages. A recent development in the combination of high-throughput screening microscopy with single cell profiling provides an unprecedented opportunity to decipher the underlying mechanisms of disease-driving phenotypes observed under a microscope. However, accurately and instantly processing large amounts of image data like longitudinal time lapse movies remains a technical challenge when an immediate analysis output (in minutes) of quantitative characterizations is required after data acquisition. Here we present a Fast and Accurate real-time Cell Tracking (FACT) algorithm, which combines GPU-based, ground truth-assisted trainable Weka segmentation and real-time Gaussian mixture model-based cell linking. FACT also implements an automatic cell track correction function to improve the tracking accuracy. With FACT, we can segment ~20,000 cells in 2 seconds (~4.5-27.5 times faster than state-of-the-art), and can export quantifiable features from the cell tracking results minutes after data acquisition (independent of the number of acquired image frames) with average 90-95% tracking precision. Such performance is not feasible with state-of-the-art cell tracking algorithms. We applied FACT to real-time identify directionally migrating glioblastoma cells with 96% precision and to identify rare, irregular cell lineages in a population of ~10,000 cells from a 24hr-time lapse movie with an average 91% F1 score, results from both were exported instantly, mere minutes after image acquisition.

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Section: Fact Identifies Abnormal Cancer Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Chou

Beerens

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Using this approach, they uncovered cellular phenotypes and dysregulated pathways with automated single cell laser microdissection coupled with ultra-high sensitivity mass-spec in melanoma progression. This system is applicable to any biological system that can be imaged and similar to another application recently described, called FUNpro ( 65 ). FUNpro also uses a microscopy-based method, coupled with SCOPE-MS, to perform functional single-cell proteomic-profiling in order to link the proteome to cellular phenotype.…”

Section: Challenges With Proteomics and Future Prospects: Single-cell...mentioning

confidence: 99%

Emerging insights and challenges for understanding T cell function through the proteome

Solt

2022

Front. Immunol.

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T cells rapidly transition from a quiescent state into active proliferation and effector function upon exposure to cognate antigen. These processes are tightly controlled by signal transduction pathways that influence changes in chromatin remodeling, gene transcription, and metabolism, all of which collectively drive specific T cell memory or effector cell development. Dysregulation of any of these events can mediate disease and the past several years has shown unprecedented novel approaches to understand these events, down to the single-cell level. The massive explosion of sequencing approaches to assess the genome and transcriptome at the single cell level has transformed our understanding of T cell activation, developmental potential, and effector function under normal and various disease states. Despite these advances, there remains a significant dearth of information regarding how these events are translated to the protein level. For example, resolution of protein isoforms and/or specific post-translational modifications mediating T cell function remains obscure. The application of proteomics can change that, enabling significant insights into molecular mechanisms that regulate T cell function. However, unlike genomic approaches that have enabled exquisite visualization of T cell dynamics at the mRNA and chromatin level, proteomic approaches, including those at the single-cell level, has significantly lagged. In this review, we describe recent studies that have enabled a better understanding of how protein synthesis and degradation change during T cell activation and acquisition of effector function. We also highlight technical advances and how these could be applied to T cell biology. Finally, we discuss future needs to expand upon our current knowledge of T cell proteomes and disease.

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Proteomics mining of cancer hallmarks on a single‐cell resolution

Zuo

Yang

et al. 2023

Mass Spectrometry Reviews

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Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population‐averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single‐cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single‐cell proteomics, herein we review several novel technologies with particular focus on single‐cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single‐cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

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Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics

Cited by 11 publications

References 25 publications

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Emerging insights and challenges for understanding T cell function through the proteome

Proteomics mining of cancer hallmarks on a single‐cell resolution

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