Recommendations, guidelines, and best practice for the use of human induced pluripotent stem cells for neuropharmacological studies of neuropsychiatric disorders

The number of individuals suffering from neuropsychiatric disorders (NPDs) has increased worldwide, with 3 million disability-adjusted life-years calculated in 2019. Though research using various approaches including genetics, imaging, clinical and animal models has advanced our knowledge regarding NPDs, we still lack basic knowledge regarding the underlying pathophysiological mechanisms. Moreover, there is an urgent need for highly effective therapeutics for NPDs i. Human induced pluripotent stem cells (hiPSCs) generated from somatic cells enabled scientists to create brain cells in a patient-specific manner. However, there are challenges to the use of hiPSCs that need to be addressed. In the current paper, consideration of best practices for neuropharmacological and neuropsychiatric research using hiPSCs will be discussed. Specifically, we provide recommendations for best practice in patient recruitment, including collecting demographic, clinical, medical (before and after treatment and response), diagnostic (incl. scales) and genetic data from the donors. We highlight considerations regarding donor genetics and sex, in addition to discussing biological and technical replicates. Furthermore, we present our views on selecting control groups/lines, experimental designs, and considerations for conducting neuropharmacological studies using hiPSC-based models in the context of NPDs. In doing so, we explore key issues in the field concerning reproducibility, statistical analysis, and how to translate in vitro studies into clinically relevant observations. The aim of this article is to provide a key resource for hiPSC researchers to perform robust and reproducible neuropharmacological studies, with the ultimate aim of improving identification and clinical translation of novel therapeutic drugs for NPDs

Publisher Correction: LifeTime and improving European healthcare through cell-based interceptive medicine.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03287-8.</jats:p

A Combination of CRISPR/Cas9 and Standardized RNAi as a Versatile Platform for the Characterization of Gene Function

Traditional loss-of-function studies in Drosophila suffer from a number of shortcomings, including off-target effects in the case of RNA interference (RNAi) or the stochastic nature of mosaic clonal analysis. Here, we describe minimal in vivo GFP interference (miGFPi) as a versatile strategy to characterize gene function and to conduct highly stringent, cell type-specific loss-of-function experiments in Drosophila. miGFPi combines CRISPR/Cas9-mediated tagging of genes at their endogenous locus with an immunotag and an exogenous 21 nucleotide RNAi effector sequence with the use of a single reagent, highly validated RNAi line targeting this sequence. We demonstrate the utility and time effectiveness of this method by characterizing the function of the Polymerase I (Pol I)-associated transcription factor Tif-1a, and the previously uncharacterized gene MESR4, in the Drosophila female germline stem cell lineage. In addition, we show that miGFPi serves as a powerful technique to functionally characterize individual isoforms of a gene. We exemplify this aspect of miGFPi by studying isoform-specific loss-of-function phenotypes of the longitudinals lacking (lola) gene in neural stem cells. Altogether, the miGFPi strategy constitutes a generalized loss-of-function approach that is amenable to the study of the function of all genes in the genome in a stringent and highly time effective manner

Organoid sizes.

Organoids sizes measured using fluorescence-activated cell sorting (FACS) and using an image-based method. (XLSX)</p

Summary statistics of the posterior distribution.

Different summary statistics for the 1,000 samples of the posterior distribution we obtained using MCMC. (XLSX)</p

Lineages switch from fast to slow growth.

(Experimental data from Esk et al., 2020. The error bars (panels C, E, F) and shaded areas (panel D) show ± two standard deviations across the three replicates) (A) Observed lineage size distributions at different time points (only replicate 1 shown, others are similar). (B) Relative frequencies of different lineage sizes on day 40 vs. Pareto power law with equality index . (C) Convergence of Pareto equality index to α = 0.46 (dotted line, represents the average from day 11 onward). Per-replicate estimates are listed in S5 Table. (D) Observed rank-size distributions, threshold size lTh (horizontal dotted line) and threshold rank (vertical dotted line). Threshold size and rank mark the truncation point of the Zipfian power law r-1/α separating fast- and slow-growing lineages. (E) Size threshold lTh and (F) number of fast-growing lineages (threshold rank) over time. Per-replicate estimated are listed in S2 Table (G) Over time, the size of fast-growing lineages grows but their number drops, causing the lineage size distribution to approach a power law.</p

Pareto equality indices.

Estimates Pareto equality indices α for each replicate. The column μ contains the mean across the replicates for each day, μ−2σ and μ−2σ contain the mean plus/minus two standard deviations. (XLSX)</p