Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

André B. Cunha; Jie Hou; Christin Schuelke

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Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

André B. Cunha

Jie Hou

Christin Schuelke

| 31 dic 2019

Journal of Electrical Bioimpedance

Volume 10 (2019): Numero 1 (January 2019)

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Pubblicato online: 31 dic 2019

Pagine: 124 - 132

Ricevuto: 01 dic 2019

DOI: https://doi.org/10.2478/joeb-2019-0018

Parole chiave
machine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory

© 2019 André B. Cunha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Relative impedance spectra for the three cell lines for proliferation (green) and differentiation (blue) on Day 20.

1w54e-Microelectrode array with 54 measurement electrodes in a single well format. Arrangement of electrodes with diameter and horizontal and vertical spacing shown in mm.

Accuracy and loss data for both training and testing (day10) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

Training	4603c27				IMR90c01				IMR90-4
Day	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss
20	1553	1514	1	0	1517	1519	1	0.02	1552	1510	0.91	0.35
Testing
Day	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss
10	1520	1564	0.78	0.65	1555	2021	0.66	0.60	2020	1516	0.87	0.60
	2012	1564	0.74	0.56	2042	2021	0.57	0.77	2022	1516	0.80	0.62
	2041	1564	0.81	0.57	2050	2021	0.64	0.90	2055	1516	0.90	0.55
	2044	1564	0.72	0.60	2053	2021	0.64	0.57	2020	1556	0.88	0.59
	1520	2047	0.77	0.70	1555	2048	0.67	0.63	2022	1556	0.81	0.62
	2012	2047	0.73	0.62	2042	2048	0.57	0.79	2055	1556	0.92	0.55
	2041	2047	0.80	0.62	2050	2048	0.65	0.92	2020	2010	0.89	0.59
	2044	2047	0.70	0.65	2053	2048	0.65	0.60	2022	2010	0.82	0.61
									2055	2010	0.93	0.54
									2020	2013	0.86	0.60
									2022	2013	0.79	0.62
									2055	2013	0.89	0.56
									2020	2014	0.87	0.63
									2022	2014	0.80	0.65
									2055	2014	0.91	0.58
Average			0.76	0.62			0.63	0.72			0.86	0.59

j.joeb-2019-0018.tab.003.w2aab3b7d245b1b6b1ab2b4b1b1Aa

Component	Concentration

	NIM	NPM
Advanced DMEM/F12 : Neurobasal^™	1:1	1:1
B-27^™ Supplement	2 % (v/v)	2 % (v/v)
N-2 Supplement	1 % (v/v)	1 % (v/v)
GlutaMax^™	1 % (v/v)	1 % (v/v)
LIF	10 ng/ml	10 ng/ml
CHIR99021	4 μM	3 μM
SB431542	3 μM	2 μM
Compound E	0.1 μM	-
BSA	0.0005 % (w/v)	0.0005 % (w/v)

Accuracy and loss data for both training and testing (day 20) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

Training	4603c27				IMR90c01				IMR90-4
Day	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss
20	1553	1514	1	0	1517	1519	1	0.02	1552	1510	0.91	0.35
Testing
Day	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss	ID (Diff.)	ID (Prol.)	Accuracy	Loss
20	1520	1564	0.88	0.56	1555	2021	0.78	0.64	2020	1516	0.87	0.50
	2012	1564	0.87	0.77	2042	2021	0.74	0.56	2022	1516	0.96	0.45
	2041	1564	0.94	0.22	2050	2021	0.82	0.57	2055	1516	0.95	0.42
	2044	1564	0.97	0.15	2053	2021	0.72	0.60	2020	1556	0.81	0.53
	1520	2047	0.86	0.55	1555	2048	0.77	0.77	2022	1556	0.93	0.47
	2012	2047	0.85	0.79	2042	2048	0.73	0.73	2055	1556	0.91	0.44
	2041	2047	0.93	0.16	2050	2048	0.81	0.80	2020	2010	0.86	0.52
	2044	2047	0.97	0.82	2053	2048	0.71	0.71	2022	2010	0.97	0.46
									2055	2010	0.96	0.43
									2020	2013	0.83	0.55
									2022	2013	0.93	0.48
									2055	2013	0.92	0.46
									2020	2014	0.84	0.58
									2022	2014	0.95	0.51
									2055	2014	0.93	0.48
Average			0.91	0.50			0.76	0.67			0.91	0.47

j.joeb-2019-0018.tab.004.w2aab3b7d245b1b6b1ab2b4b2b1Aa

Component	Concentration
Astrocyte proliferation medium

Neurobasal™
FBS	10 % (v/v)
GlutaMax™	1.5 % (v/v)
Pen/Strep	0.2 % (v/v)
Gentamicin	0.04 % (v/v)

Astro-NDM

DMEM/F12
Apotransferrin	0.0095 % (w/v)
Ascorbic acid	300 μM
cAMP	300 nM
Glucose monohydrate	33.3 mM
GlutaMax™	1 % (v/v)
Heparin	0.0004 % (v/v)
HEPES	5 mM
Insulin, human	0.0025 % (w/v)
NaHCO₃	3 mM
Sodium selenite	30 nM
Progesterone	20 nM
Putrescine	60 nM
Pen/Strep	0.2 % (v/v)
Gentamicin	0.04 % (v/v)

eISSN:: 1891-5469
Lingua:: Inglese

Frequenza di pubblicazione:: Volume Open
Argomenti della rivista:: Engineering, Bioengineering and Biomedical Engineering, Biomedical Electronics, Life Sciences, Biophysics, Medicine, Biomedical Engineering, Physics, Spectroscopy and Metrology

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Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Pubblicato online: 31 dic 2019

Pagine: 124 - 132

Ricevuto: 01 dic 2019

DOI: https://doi.org/10.2478/joeb-2019-0018

Parole chiave
machine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory

© 2019 André B. Cunha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1

Figure 2

Accuracy and loss data for both training and testing (day10) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

j.joeb-2019-0018.tab.003.w2aab3b7d245b1b6b1ab2b4b1b1Aa

Accuracy and loss data for both training and testing (day 20) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

j.joeb-2019-0018.tab.004.w2aab3b7d245b1b6b1ab2b4b2b1Aa

Machine learning for stem cell differentiation and proliferation classification on electrical impedance spectroscopy

Pubblicato online: 31 dic 2019

Pagine: 124 - 132

Ricevuto: 01 dic 2019

DOI: https://doi.org/10.2478/joeb-2019-0018

Parole chiavemachine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory

© 2019 André B. Cunha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1

Figure 2

Accuracy and loss data for both training and testing (day10) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

j.joeb-2019-0018.tab.003.w2aab3b7d245b1b6b1ab2b4b1b1Aa

Accuracy and loss data for both training and testing (day 20) for day 20 cell models. Maximum (green) and minimum (red) values signalled for each cell model.

j.joeb-2019-0018.tab.004.w2aab3b7d245b1b6b1ab2b4b2b1Aa

Parole chiave
machine learning, electrical impedance spectroscopy, stem cells, differentiation, proliferation, recurrent neural networks, long term short term memory