We thank Qiushi Sun, Yao Wang, Yu Xia, Jinyu Yang, Chang Lin, Zimu Cen, Dongdong Liang, Rong Wei, Ze Xu, Guangyin Xi, Gang Xue, Can Ye, Li-Peng Wang, Peng Zou, Shi-Qiang Wang, Pablo Rivera-Fuentes, Salome P&#252;ntener, Zhixing Chen, Yi Liu, and Jue Zhang, for laying the groundwork of this strategy. This work was supported by the National Key R&#38;D Program of China (2018YFA0800504, 2019YFA0110000) and the Space Medical Experiment Project of China Manned Space Program (HYZHXM01020) to Yang Zhao. Figure 1 was created with BioRender.com.

Monitoring Pluripotent Stem Cell Differentiation by Image-Based ML Strategy

Yang Zhao, Jue Zhang, Xiaochun Yang, Yao Wang, and Daichao Chen are filing a patent for the PSC differentiation strategy reported in this paper (202210525166.X).

Here, we described a detailed protocol to overcome one of the major problems in current PSC application and translation&#8212;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...

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 applications1,2,3. 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, modeling of heart disease, and cell transplantation8,9,10,11.
However, the conversion from PSC to the terminal differentiated cells is a stepwise process, and multiple perturbations during the differentiation process may lead cells to divergent cell fates. Different genetic backgrounds and epigenetic marks of PSC lines influence the potential for differentiation to a specific lineage12,13,14,15; the number of PSC passages and accumulated gene mutations are also sources of PSC heterogeneity; differences in the experimental operations employed by different experimenters can also lead to completely different differentiation results16,17,18,19,20. Therefore, currently one of the main problems in PSC-derived cell production is the instability among cell lines and batches21,22,23,24,25. Instability in PSC differentiation often leads to multiple repeated experiments, consuming significant time and labor resources. To address this issue, it is crucial to develop a strategy that minimizes the variability among cell lines and batches, thus enhancing the stability and robustness of the differentiation.
Recently, advances in high-resolution microscopy and machine learning (ML) have facilitated the application of ML-based quantitative image analysis in cell biology, making it possible to utilize valuable information in cell imaging features26,27,28,29,30,31,32,33,34. In our previous work, we proposed a live-cell image-based ML strategy to monitor and intervene in the PSC differentiation status in real time to improve the stability and efficiency of the PSC differentiation (Figure 1)35. Taking PSC-to-cardiomyocyte differentiation as an example, we evaluated the initial PSC state using random forest models, predicted the optimal differentiation condition using logistic regression models, and recognized successfully differentiated cells using deep learning-based Grad-CAM36 and pix2pix37. ML models learned to identify cell lineages from a range of bright-field morphological features, including features about area, circumference, convexity, solidity, brightness, moving velocity, and other implicit features extracted by deep convolutional neural networks. Based on inference from these established ML models, we realized control of the initial PSC state, early assessment and intervention in differentiation conditions, and elimination of the misdifferentiated cell contamination, together providing a comprehensive and accurate modulation of the cardiac differentiation process. Here we provide a step-by-step protocol for developing the strategy.

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

a live-cell image-based machine learning strategy to monitor pluripotent stem cell differentiation

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine.&#160;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.

&#160;Based on brightfield imaging and ML, the overall differentiation process can be intelligently monitored and optimized. At the PSC stage, we developed an ML model that could predict the final differentiation efficiency according to the morphological features of initial PSC colonies, to determine the most suitable or appropriate time point to initiate differentiation (Figure 4A,B). The differentiation efficiency predicted by the random forest model is highly correlated w...

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

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.

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

Pluripotent stem cell (PSC) technologies have been widely used in drug discovery, disease modeling, and regenerative medicine.&#160;However, available ...

a-live-cell-image-based-machine-learning-strategy-to-monitor

Research

JoVE Journal

Biology

475 Views.  Peking University. In deze studie, gebaseerd op live-cell bright-field-beelden, ontwikkelden we een strategie, waarbij we gebruik maakten van verschillende machine learning-modellen. Deze strategie kan de celafstamming op niet-invasieve wijze identificeren, het differentiatieproces in realtime moduleren en het differentiatieprotocol optimaliseren, waardoor de onkwetsbaarheid in PSC-voor-functionele celdifferentiatie wordt verbeterd. Pluripotente stamcellen bieden het vermogen o...

Video: Auteur in de schijnwerpers: Verbetering van PSC-naar-functionele celdifferentiatie met behulp van ML-modellen op basis van live-cell bright-field beeldvorming

Stencil Micropatterning of Human Pluripotent Stem Cells for Probing Spatial Organization of Differentiation Fates

Human pluripotent stem cells (hPSCs), including embryonic stem cells and induced pluripotent stem cells, have the intrinsic ability to differentiate into all three germ layers. This makes them an attractive cell source for regenerative medicine and experimental modeling of normal and diseased organogenesis. However, the differentiation of hPSCs in vitro is heterogeneous and spatially disordered. Cell micropatterning technologies potentially offer the means to spatially control stem cell microenvironments and organize the resultant differentiation fates. Micropatterning hPSCs needs to take into account the stringent requirements for hPSC survival and maintenance. Here, we describe stencil micropatterning as a method that is highly compatible with hPSCs. hPSC micropatterns are specified by the geometries of the cell stencil through-holes, which physically confine the locations where hPSCs can access and attach to the underlying extracellular matrix-coated substrate. Due to this mode of operation, there is greater flexibility to use substrates that can adequately support hPSCs as compared to other cell micropatterning methods. We also highlight critical steps for the successful generation of hPSC micropatterns. As an example, we demonstrate that stencil micropatterning of hPSCs can be used to modulate spatial polarization of cell-cell and cell-matrix adhesions, which in turn determines mesoendoderm differentiation patterns. This simple and robust method to micropattern hPSCs widens the prospects of establishing experimental models to investigate tissue organization and patterning during early embryonic development.

Human pluripotent stem cells (hPSCs) have the intrinsic ability to differentiate and self-organize into distinct tissue patterns; although this requires the presentation of spatial environmental gradients. We present stencil micropatterning as a simple and robust method to generate biochemical and mechanical gradients for controlling hPSC differentiation patterns.

Human pluripotent stem cells (hPSCs), including embryonic stem cells and induced pluripotent stem cells, have the intrinsic ability to differentiate into ...

Developmental Biology

Development, Expansion, and In vivo Monitoring of Human NK Cells from Human Embryonic Stem Cells (hESCs) and Induced Pluripotent Stem Cells (iPSCs)

We present a method for deriving natural killer (NK) cells from undifferentiated hESCs and iPSCs using a feeder-free approach. This method gives rise to high levels of NK cells after 4 weeks culture and can undergo further 2-log expansion with artificial antigen presenting cells. hESC- and iPSC-derived NK cells developed in this system have a mature phenotype and function. The production of large numbers of genetically modifiable NK cells is applicable for both basic mechanistic as well as anti-tumor studies. Expression of firefly luciferase in hESC-derived NK cells allows a non-invasive approach to follow NK cell engraftment, distribution, and function. We also describe a dual-imaging scheme that allows separate monitoring of two different cell populations to more distinctly characterize their interactions in vivo. This method of derivation, expansion, and dual in vivo imaging provides a reliable approach for producing NK cells and their evaluation which is necessary to improve current NK cell adoptive therapies.

Development, Expansion, and In vivo Monitoring of Human NK Cells from Human Embryonic Stem Cells hESCs and Induced Pluripotent Stem Cells iPSCs

This protocol describes the development, expansion, and in vivo imaging of NK cells derived from hESCs and iPSCs.

We present a method for deriving natural killer (NK) cells from undifferentiated hESCs and iPSCs using a feeder-free approach. This method gives rise to ...

Bioengineering

Transient Treatment of Human Pluripotent Stem Cells with DMSO to Promote Differentiation

Despite the growing use of pluripotent stem cells (PSCs), challenges in efficiently differentiating embryonic and induced pluripotent stem cells (ESCs and iPSCs) across various lineages remain. Numerous differentiation protocols have been developed, yet variability across cell lines and low rates of differentiation impart challenges in successfully implementing these protocols. Described here is an easy and inexpensive means to enhance the differentiation capacity of PSCs. It has been previously shown that treatment of stem cells with a low concentration of dimethyl sulfoxide (DMSO) significantly increases the propensity of a variety of PSCs to differentiate to different cell types following directed differentiation. This technique has now been shown to be effective across different species (e.g., mouse, primate, and human) into multiple lineages, ranging from neurons and cortical spheroids to smooth muscle cells and hepatocytes. The DMSO pretreatment improves PSC differentiation by regulating the cell cycle and priming stem cells to be more responsive to differentiation signals. Provided here is the detailed methodology for using this simple tool as a reproducible and widely applicable means to more efficiently differentiate PSCs to any lineage of choice.

Generating differentiated cell types from human pluripotent stem cells (hPSCs) holds great therapeutic promise but remains challenging. PSCs often exhibit an inherent inability to differentiate even when stimulated with a proper set of signals. Described here is a simple tool to enhance multilineage differentiation across a variety of PSC lines.

Despite the growing use of pluripotent stem cells (PSCs), challenges in efficiently differentiating embryonic and induced pluripotent stem cells (ESCs and ...

Differentiation of Embryonic Stem Cells into Oligodendrocyte Precursors

Oligodendrocytes are the myelinating cells of the central nervous system. For regenerative cell therapy in demyelinating diseases, there is significant interest in deriving a pure population of lineage-committed oligodendrocyte precursor cells (OPCs) for transplantation. OPCs are characterized by the activity of the transcription factor Olig2 and surface expression of a proteoglycan NG2. Using the GFP-Olig2 (G-Olig2) mouse embryonic stem cell (mESC) reporter line, we optimized conditions for the differentiation of mESCs into GFP+Olig2+NG2+ OPCs. In our protocol, we first describe the generation of embryoid bodies (EBs) from mESCs. Second, we describe treatment of mESC-derived EBs with small molecules: (1) retinoic acid (RA) and (2) a sonic hedgehog (Shh) agonist purmorphamine (Pur) under defined culture conditions to direct EB differentiation into the oligodendroglial lineage. By this approach, OPCs can be obtained with high efficiency (&gt;80%) in a time period of 30 days. Cells derived from mESCs in this protocol are phenotypically similar to OPCs derived from primary tissue culture. The mESC-derived OPCs do not show the spiking property described for a subpopulation of brain OPCs in situ. To study this electrophysiological property, we describe the generation of spiking mESC-derived OPCs by ectopically expressing NaV1.2 subunit. The spiking and nonspiking cells obtained from this protocol will help advance functional studies on the two subpopulations of OPCs.

We describe a small molecule-based protocol for differentiation of mouse embryonic stem cells into oligodendrocyte precursor cells (OPCs). This protocol generates Olig2+NG2+ OPCs with high efficiency by 30 days of differentiation. We also describe a method to generate "spiking" OPCs that can fire action potentials.

Oligodendrocytes are the myelinating cells of the central nervous system. For regenerative cell therapy in demyelinating diseases, there is significant ...

Live Cell Response to Mechanical Stimulation Studied by Integrated Optical and Atomic Force Microscopy

To understand the mechanism by which living cells sense mechanical forces, and how they respond and adapt to their environment, a new technology able to investigate cells behavior at sub-cellular level with high spatial and temporal resolution was developed. Thus, an atomic force microscope (AFM) was integrated with total internal reflection fluorescence (TIRF) microscopy and fast-spinning disk (FSD) confocal microscopy. The integrated system is broadly applicable across a wide range of molecular dynamic studies in any adherent live cells, allowing direct optical imaging of cell responses to mechanical stimulation in real-time. Significant rearrangement of the actin filaments and focal adhesions was shown due to local mechanical stimulation at the apical cell surface that induced changes into the cellular structure throughout the cell body. These innovative techniques will provide new information for understanding live cell restructuring and dynamics in response to mechanical force. A detailed protocol and a representative data set that show live cell response to mechanical stimulation are presented.

This paper aims to instruct the reader in the operation of an integrated atomic force-optical imaging microscope for mechanical stimulation of live cells in culture. A step-by-step protocol is presented. A representative data set that shows live cell response to mechanical stimulation is presented.

To understand the mechanism by which living cells sense mechanical forces, and how they respond and adapt to their environment, a new technology able to ...

Induced Pluripotent Stem Cell Differentiation Using an Automated Culture System

An Automated Culture System for Maintaining and Differentiating Human-Induced Pluripotent Stem Cells

Human induced pluripotent stem cells (hiPSCs) with infinite self-proliferating ability have been expected to have applications in numerous fields, including the elucidation of rare disease pathologies, the development of new medicines, and regenerative medicine aiming to restore damaged organs. Despite this, the social implementation of hiPSCs is still limited. This is partly because of the difficulty of reproducing differentiation in culture, even with advanced knowledge and sophisticated technical skills, due to the high sensitivity of iPSCs to minute environmental changes. The application of an automated culture system can solve this issue. Experiments with high reproducibility independent of a researcher's skill can be expected according to a shared procedure across various institutes. Although several automated culture systems that can maintain iPSC cultures and induce differentiation have been developed previously, these systems are heavy, large, and costly because they make use of humanized, multi-articulated robotic arms. To improve on the above issues, we developed a new system using a simple x-y-z axis slide rail system, allowing it to be more compact, lighter, and cheaper. Furthermore, the user can easily modify parameters in the new system to develop new handling tasks. Once a task is established, all the user needs to do is prepare the iPSC, supply the reagents and consumables needed for the desired task in advance, select the task number, and specify the time. We confirmed that the system could maintain iPSCs in an undifferentiated state through several passages without feeder cells and differentiate into various cell types, including cardiomyocytes, hepatocytes, neural progenitors, and keratinocytes. The system will enable highly reproducible experiments across institutions without the need for skilled researchers and will facilitate the social implementation of hiPSCs in a wider range of research fields by diminishing the obstacles for new entries.

Author Spotlight: Automating iPSC Culture for Enhanced Reproducibility 

Here, we present a protocol for an automated cell culture system. This automated culture system reduces labor and benefits the users, including researchers unfamiliar with handling induced pluripotent stem (iPS) cells, from the maintenance of iPS cells to differentiation into various types of cells.

Human induced pluripotent stem cells (hiPSCs) with infinite self-proliferating ability have been expected to have applications in numerous fields, ...

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate ...

Neuroscience

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the heart, and improving strategies to accurately model heart disease. The potential of exploiting human induced pluripotent stem cell (hiPSC) technology to generate cardiac muscle &#8220;in a dish&#8221; for these applications continues to generate high enthusiasm. In recent years, the ability to efficiently generate cardiomyogenic cells from human pluripotent stem cells (hPSCs) has greatly improved, offering us new opportunities to model very early stages of human cardiac development not otherwise accessible. In contrast to many previous methods, the cardiomyocyte differentiation protocol described here does not require cell aggregation or the addition of Activin A or BMP4 and robustly generates cultures of cells that are highly positive for cardiac troponin I and T (TNNI3, TNNT2), iroquois-class homeodomain protein IRX-4 (IRX4), myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC2v) and myosin regulatory light chain 2, atrial isoform (MLC2a) by day 10 across all human embryonic stem cell (hESC) and hiPSC lines tested to date. Cells can be passaged and maintained for more than 90 days in culture. The strategy is technically simple to implement and cost-effective. Characterization of cardiomyocytes derived from pluripotent cells often includes the analysis of reference markers, both at the mRNA and protein level. For protein analysis, flow cytometry is a powerful analytical tool for assessing quality of cells in culture and determining subpopulation homogeneity. However, technical variation in sample preparation can significantly affect quality of flow cytometry data. Thus, standardization of staining protocols should facilitate comparisons among various differentiation strategies. Accordingly, optimized staining protocols for the analysis of IRX4, MLC2v, MLC2a, TNNI3, and TNNT2 by flow cytometry are described.

The article describes the detailed methodology to efficiently differentiate human pluripotent stem cells into cardiomyocytes by selectively modulating the Wnt pathway, followed by flow cytometry analysis of reference markers to assess homogeneity and identity of the population.

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the ...

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

We have developed a novel protocol for studying heterocellular population dynamics in response to perturbations. This manuscript describes an imaging-based platform that produces quantitative datasets for simultaneous characterization of multiple cellular phenotypes of heterocellular populations in a robust manner.

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do ...

Automated Production of Human Induced Pluripotent Stem Cell-Derived Cortical and Dopaminergic Neurons with Integrated Live-Cell Monitoring

Manual culture and differentiation protocols for human induced pluripotent stem cells (hiPSC) are difficult to standardize, show high variability and are prone to spontaneous differentiation into unwanted cell types. The methods are labor-intensive and are not easily amenable to large-scale experiments. To overcome these limitations, we developed an automated cell culture system coupled to a high-throughput imaging system and implemented protocols for maintaining multiple hiPSC lines in parallel and neuronal differentiation. We describe the automation of a short-term differentiation protocol using Neurogenin-2 (NGN2) over-expression to produce hiPSC-derived cortical neurons within 6&#8210;8 days, and the implementation of a long-term differentiation protocol to generate hiPSC-derived midbrain dopaminergic (mDA) neurons within 65 days. Also, we applied the NGN2 approach to a small molecule-derived neural precursor cells (smNPC)&#160;transduced with GFP lentivirus and established a live-cell automated neurite outgrowth assay. We present an automated system with protocols suitable for routine hiPSC culture and differentiation into cortical and dopaminergic neurons. Our platform is suitable for long term hands-free culture and high-content/high-throughput hiPSC-based compound, RNAi and CRISPR/Cas9 screenings to identify novel disease mechanisms and drug targets.

We show the automation of human induced pluripotent stem cell (hiPSC) cultures and neuronal differentiations compatible with automated imaging and analysis.

Manual culture and differentiation protocols for human induced pluripotent stem cells (hiPSC) are difficult to standardize, show high variability and are ...

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

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