Project description:Human pluripotent stem cell (hPSC)-derived hepatocyte-like cells (HLCs) hold great promise for liver disease modeling, drug discovery, drug toxicity screens, or even regenerative therapies. Yet, several hurdles still need to be overcome, including among others decrease in the cost of goods to generate HLCs and automation of the differentiation process. We here describe that use of an automated liquid handling system results in highly reproducible HLC differentiation from hPSCs. This enabled us to screen 92 chemicals to replace expensive growth factors at each step of the differentiation protocol to reduce the cost of goods of the differentiation protocol by approximately 79%. In addition, we also evaluated several recombinant extracellular matrices (ECM) to replace Matrigel. We demonstrated that differentiation of hPSCs on Laminin-521 using an optimized small molecule combination resulted in HLCs that were transcriptionally identical to HLCs generated using current growth factor combinations. In addition, the HLCs created using the optimized small molecule combination also secreted similar concentrations of albumin and urea, and relatively low concentrations of alfa-fetoprotein (AFP), displayed similar CYP3A4 functionality and a similar drug toxicity susceptibility as HLCs generated with growth factor cocktails. The broad applicability of the new differentiation protocol was demonstrated for four different hPSC lines. This allowed the creation of a scalable, xeno-free, and cost-efficient hPSC-derived HLC culture, suitable for high throughput disease modeling and drug screenings, or even for the creation of HLCs for regenerative therapies.
Project description:Our understanding of eukaryotic gene regulation is limited by the complexity of protein-DNA interactions that comprise the chromatin landscape and inefficient methods for characterizing these interactions. We recently introduced CUT&RUN, an antibody-targeted method that profiles DNA-binding proteins, histones and chromatin modifying proteins with high sensitivity and resolution. Here we describe an automated CUT&RUN platform and apply it to profile the chromatin landscapes of human cell lines. We develop a metric to quantitatively compare chromatin features between cell types and identify their distinctive gene expression programs. Finally, we use this method to identify gene expression features of two different pediatric diffuse midline gliomas using frozen tissue from patient-derived xenografts. Our easy, cost-effective workflow makes automated CUT&RUN an attractive tool for high-throughput characterization of cell types and patient samples.
Project description:Drug development costs a significant amount of time and resources for new pharmaceutical drugs. However, the progress has been limited for orphan diseases such as Duchenne muscular dystrophy (DMD). By using human induced pluripotent stem cells (hiPSCs), here we show an exemplary drug screening campaign and the identification of two potential drugs effective in a DMD mouse model. We developed a DMD-hiPSC screening platform utilizing high-content imaging to identify hit compounds that enhance myogenic fusion abilities of patient-specific myoblasts. Among 1524 compounds (Johns Hopkins Clinical Compound library), two hit compounds restored in vitro fusion defects of DMD patient hiPSC-derived myoblasts. Transcriptional profiling revealed that the function of two hit compounds, ginsenoside Rd (natural product, ginseng extract) and fenofibrate (FDA-approved drug), are associated with FLT3 signaling and TGF-β signaling, respectively. The preclinical tests in mdx mice show that the treatment of the two hit compounds can ameliorate the skeletal muscle and behavioral phenotypes caused by DYSTROPHIN deficiency, suggesting therapeutic potential of these two compounds. Our study demonstrates the feasibility of early-stage drug development for rare diseases using symptom-relevant cells derived from patient-specific hiPSCs.
Project description:We present AutoRELACS, an automated implementation of the RELACS protocol using the Biomek i7 automated workstation (Beckman&Coulter). We test the performance of AutoRELACS by assessing 1) the scalability of the chromatin barcode integration step, 2) the quality of the generated data in comparison to the benchmark set by the manual protocol, and 3) the sensitivity of the automated method when working with low (≤ 25.000 cells/sample) and very low (≤ 5.000 cells/sample) cell numbers.
Project description:We demonstrate that the induction of three transcription factors (SOX10, OLIG2, NKX6.2) in hiPSC-derived neural progenitor cells (hiPSC-NPC) is sufficient to rapidly generate O4+ oligodendrocytes with an efficiency of 60 to 70% within 28 days.
Project description:The increased usage of whole-genome selection (WGS) and other molecular evaluation methods in plant breeding relies on the ability to genotype a very large number of untested individuals in each breeding cycle. Many plant breeding programs evaluate large biparental populations of homozygous individuals derived from homozygous parent inbred lines. This structure lends itself to parent-progeny imputation, which transfers the genotype scores of the parents to progeny individuals that are genotyped for a much smaller number of loci. Here we introduce a parent-progeny imputation method that infers individual genotypes from non-barcoded pooled samples of DNA of multiple individuals using a Hidden Markov Model (HMM). We demonstrate the method for pools of simulated maize double haploids (DH) from biparental populations, genotyped using a genotyping by sequencing (GBS) approach for 3,000 loci at 0.125x to 4x coverage. We observed high concordance between true and imputed marker scores and the HMM produced well-calibrated genotype probabilities that correctly reflected the uncertainty of the imputed scores. Genomic estimated breeding values (GEBV) calculated from the imputed scores closely matched GEBV calculated from the true marker scores. The within-population correlation between these sets of GEBV approached 0.95 at 1x and 4x coverage when pooling two or four individuals, respectively. Our approach can reduce the genotyping cost per individual by a factor up to the number of pooled individuals in GBS applications without the need for extra sequencing coverage, thereby enabling cost-effective large scale genotyping for applications such as WGS in plant breeding.
Project description:We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.