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

Cunha, André B.; Hou, Jie; Schuelke, Christin

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

Journal of Electrical Bioimpedance

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

By:

André B. Cunha, Jie Hou and Christin Schuelke

Open Access

|Dec 2019

Figures & Tables

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)

DOI: https://doi.org/10.2478/joeb-2019-0018 | Journal eISSN: 1891-5469

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Language: English

Page range: 124 - 132

Submitted on: Dec 1, 2019

Published on: Dec 31, 2019

Published by: University of Oslo

In partnership with: Paradigm Publishing Services

Publication frequency: 1 times per year

Keywords:

machine learning,

electrical impedance spectroscopy,

stem cells,

differentiation,

proliferation,

recurrent neural networks,

long term short term memory

Related subjects:

Engineering,

Bioengineering and biomedical engineering,

Biomedical electronics,

Life sciences,

Biophysics,

Medicine,

Biomedical engineering,

Physics,

Spectroscopy and metrology

© 2019 André B. Cunha, Jie Hou, Christin Schuelke, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Figures & Tables

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

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