Cargando…

Randomized gates eliminate bias in sort‐seq assays

Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence,...

Descripción completa

Detalles Bibliográficos
Autores principales: Trippe, Brian L., Huang, Buwei, DeBenedictis, Erika A., Coventry, Brian, Bhattacharya, Nicholas, Yang, Kevin K., Baker, David, Crawford, Lorin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601873/
http://dx.doi.org/10.1002/pro.4401
_version_ 1784817172355219456
author Trippe, Brian L.
Huang, Buwei
DeBenedictis, Erika A.
Coventry, Brian
Bhattacharya, Nicholas
Yang, Kevin K.
Baker, David
Crawford, Lorin
author_facet Trippe, Brian L.
Huang, Buwei
DeBenedictis, Erika A.
Coventry, Brian
Bhattacharya, Nicholas
Yang, Kevin K.
Baker, David
Crawford, Lorin
author_sort Trippe, Brian L.
collection PubMed
description Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi‐bin sorters.
format Online
Article
Text
id pubmed-9601873
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher John Wiley & Sons, Inc.
record_format MEDLINE/PubMed
spelling pubmed-96018732022-10-27 Randomized gates eliminate bias in sort‐seq assays Trippe, Brian L. Huang, Buwei DeBenedictis, Erika A. Coventry, Brian Bhattacharya, Nicholas Yang, Kevin K. Baker, David Crawford, Lorin Protein Sci Methods and Applications Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi‐bin sorters. John Wiley & Sons, Inc. 2022-08-30 2022-09 /pmc/articles/PMC9601873/ http://dx.doi.org/10.1002/pro.4401 Text en © 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Methods and Applications
Trippe, Brian L.
Huang, Buwei
DeBenedictis, Erika A.
Coventry, Brian
Bhattacharya, Nicholas
Yang, Kevin K.
Baker, David
Crawford, Lorin
Randomized gates eliminate bias in sort‐seq assays
title Randomized gates eliminate bias in sort‐seq assays
title_full Randomized gates eliminate bias in sort‐seq assays
title_fullStr Randomized gates eliminate bias in sort‐seq assays
title_full_unstemmed Randomized gates eliminate bias in sort‐seq assays
title_short Randomized gates eliminate bias in sort‐seq assays
title_sort randomized gates eliminate bias in sort‐seq assays
topic Methods and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601873/
http://dx.doi.org/10.1002/pro.4401
work_keys_str_mv AT trippebrianl randomizedgateseliminatebiasinsortseqassays
AT huangbuwei randomizedgateseliminatebiasinsortseqassays
AT debenedictiserikaa randomizedgateseliminatebiasinsortseqassays
AT coventrybrian randomizedgateseliminatebiasinsortseqassays
AT bhattacharyanicholas randomizedgateseliminatebiasinsortseqassays
AT yangkevink randomizedgateseliminatebiasinsortseqassays
AT bakerdavid randomizedgateseliminatebiasinsortseqassays
AT crawfordlorin randomizedgateseliminatebiasinsortseqassays