Overcoming bioprocess bottlenecks in the large-scale expansion of high-quality hiPSC aggregates in vertical-wheel stirred suspension bioreactors

Abstract Background Human induced pluripotent stem cells (hiPSCs) hold enormous promise in accelerating breakthroughs in understanding human development, drug screening, disease modeling, and cell and gene therapies. Their potential, however, has been bottlenecked in a mostly laboratory setting due to bioprocess challenges in the scale-up of large quantities of high-quality cells for clinical and manufacturing purposes. While several studies have investigated the production of hiPSCs in bioreactors, the use of conventional horizontal-impeller, paddle, and rocking-wave mixing mechanisms have demonstrated unfavorable hydrodynamic environments for hiPSC growth and quality maintenance. This study focused on using computational fluid dynamics (CFD) modeling to aid in characterizing and optimizing the use of vertical-wheel bioreactors for hiPSC production. Methods The vertical-wheel bioreactor was modeled with CFD simulation software Fluent at agitation rates between 20 and 100 rpm. These models produced fluid flow patterns that mapped out a hydrodynamic environment to guide in the development of hiPSC inoculation and in-vessel aggregate dissociation protocols. The effect of single-cell inoculation on aggregate formation and growth was tested at select CFD-modeled agitation rates and feeding regimes in the vertical-wheel bioreactor. An in-vessel dissociation protocol was developed through the testing of various proteolytic enzymes and agitation exposure times. Results CFD modeling demonstrated the unique flow pattern and homogeneous distribution of hydrodynamic forces produced in the vertical-wheel bioreactor, making it the opportune environment for systematic bioprocess optimization of hiPSC expansion. We developed a scalable, single-cell inoculation protocol for the culture of hiPSCs as aggregates in vertical-wheel bioreactors, achieving over 30-fold expansion in 6 days without sacrificing cell quality. We have also provided the first published protocol for in-vessel hiPSC aggregate dissociation, permitting the entire bioreactor volume to be harvested into single cells for serial passaging into larger scale reactors. Importantly, the cells harvested and re-inoculated into scaled-up vertical-wheel bioreactors not only maintained consistent growth kinetics, they maintained a normal karyotype and pluripotent characterization and function. Conclusions Taken together, these protocols provide a feasible solution for the culture of high-quality hiPSCs at a clinical and manufacturing scale by overcoming some of the major documented bioprocess bottlenecks.

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Overcoming Bioprocess Bottlenecks in the Large-Scale Expansion of High Quality hiPSC Aggregates in Vertical-Wheel Stirred Suspension Bioreactors

10.21203/rs.3.rs-63517/v2 ◽

2020 ◽

Author(s):

Breanna S Borys ◽

Tiffany Dang ◽

Tania So ◽

Leili Rohani ◽

Tamas Revay ◽

...

Keyword(s):

Single Cell ◽

Disease Modeling ◽

Proteolytic Enzymes ◽

Single Cells ◽

Scale Up ◽

Simulation Software ◽

Cfd Modeling ◽

Homogeneous Distribution ◽

High Quality ◽

Cell Inoculation

Abstract BackgroundHuman induced pluripotent stem cells (hiPSCs) hold enormous promise in accelerating breakthroughs in understanding human development, drug screening, disease modeling and cell and gene therapies. Their potential, however, has been bottlenecked in a mostly laboratory setting due to bioprocess challenges in the scale-up of large quantities of high-quality cells for clinical and manufacturing purposes. While several studies have investigated the production of hiPSCs in bioreactors, the use of conventional horizontal-impeller, paddle and rocking-wave mixing mechanisms have demonstrated unfavourable hydrodynamic environments for hiPSC growth and quality maintenance. This study focused on using computational fluid dynamics (CFD) modeling to aid in characterizing and optimizing the use of vertical-wheel bioreactors for hiPSC production.MethodsThe vertical-wheel bioreactor was modeled with CFD simulation software Fluent at agitation rates between 20rpm and 100rpm. These models produced fluid flow patterns that mapped out a hydrodynamic environment to guide in the development of hiPSC inoculation and in-vessel aggregate dissociation protocols. The effect of single-cell inoculation on aggregate formation and growth was tested at select CFD modeled agitation rates and feeding regimes in the vertical-wheel bioreactor. An in-vessel dissociation protocol was developed through the testing of various proteolytic enzymes and agitation exposure times.ResultsCFD modeling demonstrated the unique flow pattern and homogeneous distribution of hydrodynamic forces produced in the vertical-wheel bioreactor, making it the opportune environment for systematic bioprocess optimization of hiPSC expansion. We developed a scalable, single-cell inoculation protocol for the culture of hiPSCs as aggregates in vertical-wheel bioreactors, achieving over 30-fold expansion in 6 days without sacrificing cell quality. We have also provided the first published protocol for in-vessel hiPSC aggregate dissociation, permitting the entire bioreactor volume to be harvested into single-cells for serial passaging into larger scale reactors. Importantly, the cells harvested and re-inoculated into scaled-up vertical-wheel bioreactors not only maintained consistent growth kinetics, they maintained a normal karyotype and pluripotent characterization and function.ConclusionsTaken together, these protocols provide a feasible solution for the culture of high quality hiPSCs at a clinical and manufacturing scale by overcoming some of the major documented bioprocess bottlenecks.

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Overcoming Bioprocess Bottlenecks in the Large-Scale Expansion of High Quality hiPSC Aggregates in Vertical-Wheel Stirred Suspension Bioreactors

10.21203/rs.3.rs-63517/v1 ◽

2020 ◽

Author(s):

Breanna S Borys ◽

Tiffany Dang ◽

Tania So ◽

Leili Rohani ◽

Tamas Revay ◽

...

Keyword(s):

Single Cell ◽

Disease Modeling ◽

Proteolytic Enzymes ◽

Single Cells ◽

Scale Up ◽

Simulation Software ◽

Cfd Modeling ◽

Homogeneous Distribution ◽

High Quality ◽

Cell Inoculation

Abstract BackgroundHuman induced pluripotent stem cells (hiPSCs) hold enormous promise in accelerating breakthroughs in understanding human development, drug screening, disease modeling and cell and gene therapies. Their potential, however, has been bottlenecked in a mostly laboratory setting due to bioprocess challenges in the scale-up of large quantities of high-quality cells for clinical and manufacturing purposes. While several studies have investigated the production of hiPSCs in bioreactors, the use of conventional horizontal-impeller, paddle and rocking-wave mixing mechanisms have demonstrated unfavourable hydrodynamic environments for hiPSC growth and quality maintenance. This study focused on using computational fluid dynamics (CFD) modeling to aid in characterizing and optimizing the use of vertical-wheel bioreactors for hiPSC production.MethodsThe vertical-wheel bioreactor was modeled with CFD simulation software Fluent at agitation rates between 20rpm and 100rpm. These models produced fluid flow patterns that mapped out a hydrodynamic environment to guide in the development of hiPSC inoculation and in-vessel aggregate dissociation protocols. The effect of single-cell inoculation on aggregate formation and growth was tested at select CFD modeled agitation rates and feeding regimes in the vertical-wheel bioreactor. An in-vessel dissociation protocol was developed through the testing of various proteolytic enzymes and agitation exposure times.ResultsCFD modeling demonstrated the unique flow pattern and homogeneous distribution of hydrodynamic forces produced in the vertical-wheel bioreactor, making it the opportune environment for systematic bioprocess optimization of hiPSC expansion. We developed a scalable, single-cell inoculation protocol for the culture of hiPSCs as aggregates in vertical-wheel bioreactors, achieving over 30-fold expansion in 6 days without sacrificing cell quality. We have also provided the first published protocol for in-vessel hiPSC aggregate dissociation, permitting the entire bioreactor volume to be harvested into single-cells for serial passaging into larger scale reactors. Importantly, the cells harvested and re-inoculated into scaled-up vertical-wheel bioreactors not only maintained consistent growth kinetics, they maintained a normal karyotype and pluripotent characterization and function.ConclusionsTaken together, these protocols provide a feasible solution for the culture of high quality hiPSCs at a clinical and manufacturing scale by overcoming some of the major documented bioprocess bottlenecks.

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Generation of 2.5D lung bud organoid from human induced pluripotent stem cell

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-219111 ◽

2021 ◽

pp. 1-14

Author(s):

Xun Xu ◽

Yan Nie ◽

Weiwei Wang ◽

Imran Ullah ◽

Wing Tai Tung ◽

...

Keyword(s):

Single Cell ◽

Disease Modeling ◽

3D Culture ◽

Induced Pluripotent Stem Cell ◽

Patient Specific ◽

Homogeneous Distribution ◽

Cell Seeding ◽

Differentiation Potential ◽

Induced Pluripotent ◽

Defined Culture

Human induced pluripotent stem cells (hiPSCs) are a promising cell source to generate the patient-specific lung organoid given their superior differentiation potential. However, the current 3D cell culture approach is tedious and time-consuming with a low success rate and high batch-to-batch variability. Here, we explored the establishment of lung bud organoids by systematically adjusting the initial confluence levels and homogeneity of cell distribution. The efficiency of single cell seeding and clump seeding was compared. Instead of the traditional 3D culture, we established a 2.5D organoid culture to enable the direct monitoring of the internal structure via microscopy. It was found that the cell confluence and distribution prior to induction were two key parameters, which strongly affected hiPSC differentiation trajectories. Lung bud organoids with positive expression of NKX 2.1, in a single-cell seeding group with homogeneously distributed hiPSCs at 70%confluence (SC_70%_hom) or a clump seeding group with heterogeneously distributed cells at 90%confluence (CL_90%_het), can be observed as early as 9 days post induction. These results suggest that a successful lung bud organoid formation with single-cell seeding of hiPSCs requires a moderate confluence and homogeneous distribution of cells, while high confluence would be a prominent factor to promote the lung organoid formation when seeding hiPSCs as clumps. 2.5D organoids generated with defined culture conditions could become a simple, efficient, and valuable tool facilitating drug screening, disease modeling and personalized medicine.

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SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data

10.1101/393256 ◽

2018 ◽

Author(s):

Ivan Vogel ◽

Robert C. Blanshard ◽

Eva R. Hoffmann

Keyword(s):

Single Cell ◽

Cell Lines ◽

Whole Genome Amplification ◽

Single Cells ◽

Snp Array ◽

Lineage Tracing ◽

Whole Genome ◽

High Confidence ◽

High Quality ◽

Genome Amplification

AbstractMotivationAccurate genotyping of DNA from a single cell is required for applications such asde novomutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.ResultsIn this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC - a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single cell genotype using Bayesian [email protected]

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Optimized protocol for isolation of high-quality single cells from the female mouse reproductive tract tissues for single-cell RNA sequencing

STAR Protocols ◽

10.1016/j.xpro.2021.100970 ◽

2021 ◽

Vol 2 (4) ◽

pp. 100970

Author(s):

Rajendra Kumar Gurumurthy ◽

Naveen Kumar ◽

Cindrilla Chumduri

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Female Mouse ◽

Reproductive Tract ◽

Single Cells ◽

High Quality ◽

Optimized Protocol ◽

Single Cell Rna Sequencing

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Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applications

10.21203/rs.3.pex-905/v1 ◽

2020 ◽

Author(s):

Malosree Maitra ◽

Corina Nagy ◽

Yu Chang Wang ◽

Camila Nascimento ◽

Matthew Suderman ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Clinical Applications ◽

Post Mortem ◽

Rna Seq ◽

High Quality ◽

Limiting Step ◽

Single Nucleus ◽

Rna And Dna ◽

Variable Quality

Abstract Single-cell and single-nucleus sequencing techniques are a burgeoning field with various biological, biomedical, and clinical applications. Numerous high and low-throughput methods have been developed for sequencing the RNA and DNA content of single cells. However, for all these methods the key requirement is high quality input of a single-cell or single-nucleus suspension. Preparing such a suspension is the limiting step when working with fragile, archived tissues of variable quality. This hurdle can prevent such tissues from being extensively investigated with single-cell technologies. We describe a protocol for preparing single-nucleus suspensions within the span of a few hours that reliably works for multiple post-mortem and archived tissues types using standard lab equipment. Moreover, these preparations are compatible with single-nucleus RNA-seq and ATAC-seq using the 10X Genomics’ Chromium system.

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SureTypeSC—a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data

Bioinformatics ◽

10.1093/bioinformatics/btz412 ◽

2019 ◽

Vol 35 (23) ◽

pp. 5055-5062 ◽

Cited By ~ 2

Author(s):

Ivan Vogel ◽

Robert C Blanshard ◽

Eva R Hoffmann

Keyword(s):

Single Cell ◽

Cell Lines ◽

Whole Genome Amplification ◽

Single Cells ◽

Supplementary Information ◽

De Novo Mutation ◽

Whole Genome ◽

High Confidence ◽

High Quality ◽

Genome Amplification

Abstract Motivation Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single-cell environment is challenging due to the errors caused by whole-genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single-cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole-genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single-cell applications. Results In this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole-genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother–father–proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC—a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single-cell genotype using Bayesian statistics. Availability and implementation The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC Supplementary information Supplementary data are available at Bioinformatics online.

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Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applications

10.21203/rs.3.pex-905/v2 ◽

2020 ◽

Author(s):

Malosree Maitra ◽

Corina Nagy ◽

Yu Chang Wang ◽

Camila Nascimento ◽

Matthew Suderman ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Clinical Applications ◽

Post Mortem ◽

Rna Seq ◽

High Quality ◽

Limiting Step ◽

Single Nucleus ◽

Rna And Dna ◽

Variable Quality

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Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

10.1101/395954 ◽

2018 ◽

Cited By ~ 3

Author(s):

Alex X Lu ◽

Oren Z Kraus ◽

Sam Cooper ◽

Alan M Moses

Keyword(s):

Image Analysis ◽

Single Cell ◽

Single Cells ◽

Training Data ◽

High Quality ◽

Feature Representations ◽

Starting Point ◽

Microscopy Images ◽

Quality Features ◽

Paired Cell

AbstractCellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.Author SummaryTo understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

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