ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices

Irene De Cesare; Criseida G Zamora Chimal; Lorena Postiglione; Mahmoud Khazim; Elisa Pedone; Barbara M Shannon; Gianfranco Fiore; Giansimone Perrino; Sara Napolitano; Diego di Bernardo; Nigel J Savery; Claire S Grierson; Mario Di Bernardo; Lucia Marucci

doi:10.1021/acsomega.0c03906

ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices

Irene De Cesare
, Criseida G Zamora Chimal
, Lorena Postiglione
, Mahmoud Khazim
, Elisa Pedone
, Barbara M Shannon
, Gianfranco Fiore
, Giansimone Perrino
, Sara Napolitano
, Diego di Bernardo
, Nigel J Savery
, Claire S Grierson
, Mario Di Bernardo^*
, Lucia Marucci^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

11 Citations (Scopus)

282 Downloads (Pure)

Abstract

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

Original language	English
Pages (from-to)	2473–2476
Number of pages	4
Journal	ACS Omega
Volume	(2021) 6
Issue number	4
DOIs	https://doi.org/10.1021/acsomega.0c03906
Publication status	Published - 18 Jan 2021

Research Groups and Themes

BrisSynBio
Bristol BioDesign Institute
Engineering Mathematics Research Group

Keywords

live-cell imaging
segmentation
synthetic biology
external feedback control
microfluidics
image analysis

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COMBO: CONTROL-BASED BIODESIGN OF MAMMALIAN CELL DYNAMICS
Marucci, L. (Principal Investigator)
1/10/19 → 30/09/24
Project: Research
Unravelling the role of beta-catenin in ground state pluripotency
Marucci, L. (Principal Investigator)
1/09/16 → 29/02/20
Project: Research

Cite this

De Cesare, I., Zamora Chimal, C. G., Postiglione, L., Khazim, M., Pedone, E., Shannon, B. M., Fiore, G., Perrino, G., Napolitano, S., di Bernardo, D., Savery, N. J., Grierson, C. S., Di Bernardo, M., & Marucci, L. (2021). ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices. ACS Omega, (2021) 6(4), 2473–2476. https://doi.org/10.1021/acsomega.0c03906

@article{3edc5f53e4244ce1a2c6e44afedf71a3,

title = "ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices",

abstract = "Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.",

keywords = "live-cell imaging, segmentation, synthetic biology, external feedback control, microfluidics, image analysis",

author = "\{De Cesare\}, Irene and \{Zamora Chimal\}, \{Criseida G\} and Lorena Postiglione and Mahmoud Khazim and Elisa Pedone and Shannon, \{Barbara M\} and Gianfranco Fiore and Giansimone Perrino and Sara Napolitano and \{di Bernardo\}, Diego and Savery, \{Nigel J\} and Grierson, \{Claire S\} and \{Di Bernardo\}, Mario and Lucia Marucci",

year = "2021",

month = jan,

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doi = "10.1021/acsomega.0c03906",

language = "English",

volume = "(2021) 6",

pages = "2473–2476",

journal = "ACS Omega",

issn = "2470-1343",

publisher = "American Chemical Society",

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}

De Cesare, I, Zamora Chimal, CG, Postiglione, L, Khazim, M, Pedone, E, Shannon, BM, Fiore, G, Perrino, G, Napolitano, S, di Bernardo, D, Savery, NJ , Grierson, CS, Di Bernardo, M & Marucci, L 2021, 'ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices', ACS Omega, vol. (2021) 6, no. 4, pp. 2473–2476. https://doi.org/10.1021/acsomega.0c03906

TY - JOUR

T1 - ChipSeg

T2 - an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices

AU - De Cesare, Irene

AU - Zamora Chimal, Criseida G

AU - Postiglione, Lorena

AU - Khazim, Mahmoud

AU - Pedone, Elisa

AU - Shannon, Barbara M

AU - Fiore, Gianfranco

AU - Perrino, Giansimone

AU - Napolitano, Sara

AU - di Bernardo, Diego

AU - Savery, Nigel J

AU - Grierson, Claire S

AU - Di Bernardo, Mario

AU - Marucci, Lucia

PY - 2021/1/18

Y1 - 2021/1/18

N2 - Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

AB - Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

KW - live-cell imaging

KW - segmentation

KW - synthetic biology

KW - external feedback control

KW - microfluidics

KW - image analysis

U2 - 10.1021/acsomega.0c03906

DO - 10.1021/acsomega.0c03906

M3 - Article (Academic Journal)

C2 - 33553865

SN - 2470-1343

VL - (2021) 6

SP - 2473

EP - 2476

JO - ACS Omega

JF - ACS Omega

IS - 4

ER -

ChipSeg: an automatic tool to segment bacterial and mammalian cells cultured in microfluidic devices

Abstract

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Keywords

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COMBO: CONTROL-BASED BIODESIGN OF MAMMALIAN CELL DYNAMICS

Unravelling the role of beta-catenin in ground state pluripotency

Cite this