Bayesian cell therapy process optimization.

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFß/BMP system components in transition from embryonic stem cells to neural progenitors.

Cooperative actions of extrinsic signals and cell-intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type ß (TGFß) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell-type specific and context-dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFß system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA-expression dynamics, gene-gene interactions, and single-cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA-based perturbation of each individual component and documented the effect on steady-state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene-gene interactions display a marked cell-stage specific behavior. Furthermore, single-cell RNA-profiling at individual stages demonstrated the presence of detailed co-expression modules and subpopulations showing stable co-expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions.

Zeb2 Regulates Cell Fate at the Exit from Epiblast State in Mouse Embryonic Stem Cells.

In human embryonic stem cells (ESCs) the transcription factor Zeb2 regulates neuroectoderm versus mesendoderm formation, but it is unclear how Zeb2 affects the global transcriptional regulatory network in these cell-fate decisions. We generated Zeb2 knockout (KO) mouse ESCs, subjected them as embryoid bodies (EBs) to neural and general differentiation and carried out temporal RNA-sequencing (RNA-seq) and reduced representation bisulfite sequencing (RRBS) analysis in neural differentiation. This shows that Zeb2 acts preferentially as a transcriptional repressor associated with developmental progression and that Zeb2 KO ESCs can exit from their naïve state. However, most cells in these EBs stall in an early epiblast-like state and are impaired in both neural and mesendodermal differentiation. Genes involved in pluripotency, epithelial-to-mesenchymal transition (EMT), and DNA-(de)methylation, including Tet1, are deregulated in the absence of Zeb2. The observed elevated Tet1 levels in the mutant cells and the knowledge of previously mapped Tet1-binding sites correlate with loss-of-methylation in neural-stimulating conditions, however, after the cells initially acquired the correct DNA-methyl marks. Interestingly, cells from such Zeb2 KO EBs maintain the ability to re-adapt to 2i + LIF conditions even after prolonged differentiation, while knockdown of Tet1 partially rescues their impaired differentiation. Hence, in addition to its role in EMT, Zeb2 is critical in ESCs for exit from the epiblast state, and links the pluripotency network and DNA-methylation with irreversible commitment to differentiation. Stem Cells 2017;35:611-625.

Few Smad proteins and many Smad-interacting proteins yield multiple functions and action modes in TGFß/BMP signaling in vivo.

Signaling by the many ligands of the TGFß family strongly converges towards only five receptor-activated, intracellular Smad proteins, which fall into two classes i.e. Smad2/3 and Smad1/5/8, respectively. These Smads bind to a surprisingly high number of Smad-interacting proteins (SIPs), many of which are transcription factors (TFs) that co-operate in Smad-controlled target gene transcription in a cell type and context specific manner. A combination of functional analyses in vivo as well as in cell cultures and biochemical studies has revealed the enormous versatility of the Smad proteins. Smads and their SIPs regulate diverse molecular and cellular processes and are also directly relevant to development and disease. In this survey, we selected appropriate examples on the BMP-Smads, with emphasis on Smad1 and Smad5, and on a number of SIPs, i.e. the CPSF subunit Smicl, Ttrap (Tdp2) and Sip1 (Zeb2, Zfhx1b) from our own research carried out in three different vertebrate models.

Essential validation of gene trap mouse ES cell lines: a test case with the gene Ttrap.

Gene trapping in mouse embryonic stem (ES) cells enables near-saturation vector-based insertional mutagenesis across the genome of this model organism. About 135,000 trapped ES cell lines are made available to the scientific community by the International Gene Trap Consortium (IGTC; www.genetrap.org). A search of one of its databases identified an ES cell line (RRS512) with a betaGeo-based gene trap (gt) vector insertion in intron 5 of Ttrap, a gene that encodes an intracellular signalling protein, which is implicated in gastrulation movement and left-right asymmetry in zebrafish embryos. We have determined the exact gt insertion point in the mutant ES cell clone RRS512 and confirmed the production of a chimaeric transcript consisting of the upstream Ttrap exons and the gene trap vector encoded marker/selection fusion sequences. This ES cell line was used to generate heterozygous Ttrap mutant mice, which were further crossed to obtain Ttrap(gt/gt) mice. In contrast to Ttraps documented essential role during nodal and Smad3 controlled zebrafish early embryogenesis, Ttrap(gt/gt) mice were born with a normal Mendelian distribution. However, subsequent analysis of these Ttrap(gt/gt) mice has revealed a duplication of the wild-type Ttrap allele that was already present in the RRS512 cell line. Based on our detailed analysis presented here, we suggest an extensive procedure for the characterization of gene trap ES cell lines prior to generating gene trap mice with these.

Benchmarking the CATMA microarray. A novel tool for Arabidopsis transcriptome analysis.

Transcript profiling is crucial to study biological systems, and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the performance of the CATMA microarray designed for Arabidopsis (Arabidopsis thaliana) transcriptome analysis and compared it with the Agilent and Affymetrix commercial platforms. The CATMA array consists of gene-specific sequence tags of 150 to 500 bp, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (The Institute for Genomic Research release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled targets derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. CATMA arrays showed the largest dynamic range extending over three to four logs. Agilent and Affymetrix arrays displayed a narrower range, presumably because signal saturation occurred for transcripts at concentrations beyond 1,000 copies per cell. Sensitivity was comparable for all three platforms. For Affymetrix GeneChip data, the RMA software package outperformed Microarray Suite 5.0 for all investigated criteria, confirming that the information provided by the mismatch oligonucleotides has no added value. In addition, taking advantage of replicates in our dataset, we conducted a robust statistical analysis of the platform propensity to yield false positive and false negative differentially expressed genes, and all gave satisfactory results. The results establish the CATMA array as a mature alternative to the Affymetrix and Agilent platforms.