A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

Summary

Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.

Abstract

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine. However, available PSC-to-functional cell differentiation systems are impeded by problems of severe line-to-line and batch-to-batch variability. Precise control of cell differentiation in real time is therefore important. In this protocol, we describe a non-invasive and intelligent strategy that overcomes the variability in cell differentiation by using bright-field image-based machine learning. Taking PSC-to-cardiomyocyte differentiation as an example, this methodology provides detailed information for control of the initial PSC state, early assessment and intervention in differentiation conditions, and elimination of the misdifferentiated cell contamination, together realizing consistently high-quality differentiation from PSCs to functional cells. In principle, this strategy can be extended to other cell differentiation or reprogramming systems with multiple steps to support cell manufacturing, as well as to further our understanding of the mechanisms during cell fate conversion.

Introduction

Pluripotent stem cells (PSCs) possess the remarkable ability to differentiate into many types of cells in vitro. These differentiated functional cells could be used for cell therapy, disease modeling, and drug development, all valuable for research or clinical applications^1,2,3. For example, a variety of methods have been developed to differentiate PSCs into cardiomyocytes (CMs)^4,5,6,7. These CMs can be applied for cardiotoxicity testing of drugs, m....

Protocol

1. Cell differentiation and characterization

1. Preparation of culture reagents and culture plates
   1. Prepare PSC culture medium by adding 2 mL of supplement and 0.2% Penicillin-Streptomycin to 48 mL of basal medium. Aliquot and store the supplement at -20 °C. Store this medium at 4 °C for up to 4 weeks.
   2. Prepare PSC preparation medium by adding 1 mL of supplement and 0.2% Penicillin-Streptomycin to 500 mL of basal medium. When using, preheat the medium for one-time .......

Discussion

Here, we described a detailed protocol to overcome one of the major problems in current PSC application and translation—the variability in cell differentiation. By harnessing live-cell brightfield imaging and ML, we iteratively optimized PSC differentiation to achieve consistently high efficiency across cell lines and batches. However, in the above differentiation process, several critical steps in the protocol have a decisive influence on whether the differentiation would succeed or not. Since the cell state in th.......

Materials

Name	Company	Catalog Number	Comments
0.25% Trypsin-EDTA	Gibco	25200056	Diluted digests were used for CPC and CM digestion
4% Paraformaldehyde in PBS	KeyGEN BioTECH	KGIHC016
6-well Cell Culture Plate	NEST	703001
96-well Cell Culture Plate	NEST	701001
B27 Supplement	Gibco	17504044
B27 Supplement Minus Insulin	Gibco	A1895601
Bovine serum albumin (BSA)	GPC BIOTECH	AA904-100G
Celldiscoverer 7	Zeiss		Instruments used to take bright-field images throughout differentiation and final cTnT images
CHIR99021	Selleck	S1263
DMEM/F12	Gibco	12634010
Donkey anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 488	Thermo	A-21202	Secondary Antibody
FACSAria III	BD Biosciences		Flow cytometry sorter
Fetal Bovine Serum (FBS)	VISTECH	SE100-B
Hoechst 33342	YEASEN	40732ES03
Human Pluripotent Stem Cell Chemical-defined Medium	Cauliscell Inc	400105	Basal medium of PSC preparation medium
iPS-18	TaKaRa	Y00300
iPS-B1	Cellapy	CA4025106
iPS-F	Nuwacell	RC01001-B
iPS-M	Nuwacell	RC01001-A
IWR1-1-endo	Selleck	S7086	IWR1
Jupyter Notebook	N/A	Version 6.4.0	https://jupyter.org/
MATLAB	MathWorks	Version R2020a	Software for scientific computation and image annotation
Matrigel Matrix	Corning	354230	Matrigel
Mouse monoclonal IgG1 anti-cTnT	Thermo	MA5-12960	cTnT primary antibody
Normal Donkey Serum	Jackson	017-000-121
ORCA-Flash 4.0 V3 digital CMOS camera	Hamamatsu	C13440-20CU	The digital camera assembled on Celldiscoverer7
PBS	NEB	21-040-CVR
Penicillin-Streptomycin	Gibco	15140-122
Pluripotency Growth Mater 1 basal medium	Cellapy	CA1007500-1	Basal medium of PSC culture medium
Pluripotency Growth Mater 1 supplement	Cellapy	CA1007500-2	Supplement of PSC culture medium
Prism	Graphpad	Version 8/9	Statistical software for statistical analysis and plotting
Python	N/A	version 3.6	Python 3 environment for scientific computation, with packages pytorch (1.9.0), numpy, scipy, pandas, visdom, scikit-learn, scikit-image, opencv-python, and matplotlib software for scientific computation and image annotation.
RPMI 1640	Gibco	11875176
Supplement hPSC-CDM (500x)	Cauliscell Inc	00015	Supplement of PSC preparation medium
TiE	Nikon		An inverted fluorescence microscope (with modification) for region-selevtive purification
Triton X-100	Amresco	9002-93-1
Versene Solution	Thermo	15040066	EDTA solution for PSC digestion
Y27632	Selleck	S6390
Zen	Zeiss	Version 3.1	A supporting software of Celldiscoverer7 for  image acquisition, processing and analysis