Image-Based Live Cell Sorting

LaBelle, Cody A.; Massaro, Angelo; Cortés‐Llanos, Belén; Sims, Christopher E.; Allbritton, Nancy L.

doi:10.1016/j.tibtech.2020.10.006

Cited by 48 publications

(33 citation statements)

References 56 publications

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“…In a number of recent publications, several methods of imagebased cell sorting have been described. The ability to sort and further analyze individual cells often overcomes the loss of information that occurs when attempting to extract subtle cell-tocell variations from bulk populations 50 . With the advancements in computing power over the last decade, the ability to capture imagery and apply deep learning networks in near-real time to physically sort cells of interest based on morphological features is now possible.…”

Section: Discussionmentioning

confidence: 99%

The in vitro micronucleus assay using imaging flow cytometry and deep learning

et al. 2021

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The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream® compares well to manual microscopy and outperforms IDEAS® feature-based analysis, facilitating full automation of the MN assay.

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Section: Discussionmentioning

confidence: 99%

The in vitro micronucleus assay using imaging flow cytometry and deep learning

et al. 2021

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“…Microfluidics is another area of technology applicable to CTC detection due to portability, high-throughput capability, and precise control within the microchannel. Certain microfluidics platforms (Parsortix ® and Vycap systems) can recover CTCs based upon size and deformability instead of EMT status, and transcriptomic analysis of CTCs can be performed at the scale of a cell after isolation [267,268]. Transcriptome analysis then provides information on the state of the cell as to its position in the EMT thanks to a molecular signature by phenotype [269].…”

Section: Nanotechnology-improved Diagnostics Imaging and Treatment Monitoringmentioning

confidence: 99%

Cancer nanotechnology: current status and perspectives

2021

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Modern medicine has been waging a war on cancer for nearly a century with no tangible end in sight. Cancer treatments have significantly progressed, but the need to increase specificity and decrease systemic toxicities remains. Early diagnosis holds a key to improving prognostic outlook and patient quality of life, and diagnostic tools are on the cusp of a technological revolution. Nanotechnology has steadily expanded into the reaches of cancer chemotherapy, radiotherapy, diagnostics, and imaging, demonstrating the capacity to augment each and advance patient care. Nanomaterials provide an abundance of versatility, functionality, and applications to engineer specifically targeted cancer medicine, accurate early-detection devices, robust imaging modalities, and enhanced radiotherapy adjuvants. This review provides insights into the current clinical and pre-clinical nanotechnological applications for cancer drug therapy, diagnostics, imaging, and radiation therapy.

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“…A platform that can identify, classify, and sort living cells based on deep morphological interpretation enables integration of morphology into single-cell multi-modal analyses as a new omic, thereby greatly empowering our understanding of biology at the cellular level 25 . Facilitating molecular characterization downstream of sorting minimally perturbed cells could rede ne our understanding of cell type and state.…”

Section: Full Textmentioning

confidence: 99%

Sorting of viable unlabeled cells based on deep representations links morphology to multiomics.

Salek

Chou

et al. 2022

Preprint

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Phenotyping of single cells has dramatically lagged advances in molecular characterization and remains a manual, subjective, and destructive process. We introduce Computational Sorting and Mapping of Single cells (COSMOS), a platform for phenotyping and enrichment of cells based on deep learning interpretation of high-content morphology data in realtime. By training models on an atlas of >1.5 billion images, we demonstrate enrichment of unlabeled cells up to 33,000-fold. By applying COSMOS to tissue samples we identify malignant cells with similar accuracy to molecular approaches while enriching cells for functional evaluation. We show high-dimensional embedding vectors of morphology without any need for complex sample pre-processing, gating, or bioinformatics capabilities, which enables discovery of cellular phenotypes and integration of morphology into single-cell multimodal analyses as a new “omic”. Sorting intact cells based on deep morphological representations could further provide a powerful tool for tying together data across different modalities.

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Image-Based Live Cell Sorting

Cited by 48 publications

References 56 publications

The in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay using imaging flow cytometry and deep learning

Cancer nanotechnology: current status and perspectives

Sorting of viable unlabeled cells based on deep representations links morphology to multiomics.

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