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Image-Based Single Cell Sorting Automation in Droplet Microfluidics
The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250914/ https://www.ncbi.nlm.nih.gov/pubmed/32457421 http://dx.doi.org/10.1038/s41598-020-65483-2 |
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author | Sesen, Muhsincan Whyte, Graeme |
author_facet | Sesen, Muhsincan Whyte, Graeme |
author_sort | Sesen, Muhsincan |
collection | PubMed |
description | The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dual-camera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis. |
format | Online Article Text |
id | pubmed-7250914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72509142020-06-04 Image-Based Single Cell Sorting Automation in Droplet Microfluidics Sesen, Muhsincan Whyte, Graeme Sci Rep Article The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dual-camera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis. Nature Publishing Group UK 2020-05-26 /pmc/articles/PMC7250914/ /pubmed/32457421 http://dx.doi.org/10.1038/s41598-020-65483-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sesen, Muhsincan Whyte, Graeme Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title | Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title_full | Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title_fullStr | Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title_full_unstemmed | Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title_short | Image-Based Single Cell Sorting Automation in Droplet Microfluidics |
title_sort | image-based single cell sorting automation in droplet microfluidics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250914/ https://www.ncbi.nlm.nih.gov/pubmed/32457421 http://dx.doi.org/10.1038/s41598-020-65483-2 |
work_keys_str_mv | AT sesenmuhsincan imagebasedsinglecellsortingautomationindropletmicrofluidics AT whytegraeme imagebasedsinglecellsortingautomationindropletmicrofluidics |