Down syndrome (DS), caused by trisomy of chromosome 21, occurs in 1 of every 800 live births.Early defects in cortical development likely account for the cognitive impairments in DS, although the underlying molecular mechanism remains elusive. Here, we performed histological assays and unbiased single-cell RNA sequencing (scRNA-seq) analysis on cerebral organoids derived from four euploid cell lines and from induced pluripotent stem cells (iPSCs) from three individuals with trisomy 21 to explore cell type-specific abnormalities associated with DS during early brain development. We found that neurogenesis was significantly affected based on diminished proliferation and decreased expression of layer II and IV markers in cortical neurons in the subcortical regions; this may be responsible for the reduced size of the organoids. Furthermore, suppression of the DSCAM-PAK1 pathway which showed enhanced activities in DS) via CRISPR/Cas9, CRISPRi or small-molecule inhibitor treatment reverses abnormal neurogenesis, thereby increasing the size of organoids derived from DS iPSCs. Our study demonstrated that 3D cortical organoids developed in vitro are a valuable model of DS and provided a direct link between dysregulation of the DSCAM-PAK1 pathway and developmental brain defects in DS.

Inverse Probability Weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of "small denominators," trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this paper, we first show that the IPW estimator can have different (Gaussian or non-Gaussian) asymptotic distributions, depending on how "close to zero" the probability weights are and on how large the trimming threshold is. As a remedy, we propose an inference procedure that is robust not only to small probability weights entering the IPW estimator but also to a wide range of trimming threshold choices, by adapting to these different asymptotic distributions. This robustness is achieved by employing resampling techniques and by correcting a non-negligible trimming bias. We also propose an easy-to-implement method for choosing the trimming threshold by minimizing an empirical analogue of the asymptotic mean squared error. In addition, we show that our inference procedure remains valid with the use of a data-driven trimming threshold. We illustrate our method by revisiting a dataset from the National Supported Work program.

This supplementary material contains some technical details for the results stated in the main article and is organized as follows. Section S.1 gives some useful lemmas needed in the proofs. Section S.2 gives an additional example to illustrate the over-fitting bias issue. Section S.3 details B-Split, which is an alternative to R-Split, and offers a finite sample comparison between R-Split and B-Split. Section S.4 contains the proofs and derivations needed in Sections 3.2 and 5. Section S.5 contains the proofs for PODS discussed in Sections 4.2 and 5. Section S.6 gives sufficient conditions for Assumptions 5 and 9. Section S.7 contains the implementation details for the combined approach of R-Split and the post-double-selection method mentioned at the end of Section 5. Section S.8 contains the derivation of variance estimation in R-Split via the non-parametric delta method used in Section 3.1. In Section S.9, we discuss how regression adjustment for the average treatment effect estimation can be handled in our framework. In Section S.10, we provide an additional set of results for the analysis in Section 7 based on polynomial basis function expansions instead of spline basis expansions. Finally, Section S.11 contains the implementation details of the adaptive Lasso used in Example 1 of Section 2.2 and the simulation studies of Sections 6.

In recent years, mesenchymal stem cells (MSCs)–derived extracellular vesicles (EVs) are emerging as a potential therapeutic agent for pulmonary hypertension (PH). However, the full realization of MSCs–derived EVs therapy has been hampered by the absence of standardization in MSCs culture and the challenges of industrial scale–up. The study was to exploit an alternative replacement for MSCs using currently commercialized stem cell lines for effective targeted PH therapy. ReNcell VM—a human neural stem cell line—has been utilized here as a reliable and easily adoptable source of EVs. We first demonstrated that ReNcell-derived EVs (ReNcell-EVs) pretreatment effectively prevented Su/Hx (SU5416/hypoxia)–induced PH in mice. Then for targeted therapy, we conjugated ReNcell-EVs with CAR (CARSKNKDC) peptide (CAR-EVs)—a peptide identified to specifically target hypertensive pulmonary arteries, by bio-orthogonal chemistry. Intravenous administration of CAR-EVs selectively targeted hypertensive pulmonary artery lesions especially pulmonary artery smooth muscle cells. Moreover, compared with unmodified ReNcell-EVs, CAR-EVs treatment significantly improved therapeutic effect in reversing Su/Hx-induced PH in mice. Mechanistically, ReNcell-EVs inhibited hypoxia-induced proliferation, migration, and phenotype switch of pulmonary artery smooth muscle cells, at least in part, via the delivery of its endogenous highly expressed miRNAs, let-7b-5p, miR-92b-3p, and miR-100-5p. In addition, we also found that ReNcell-EVs inhibited hypoxia-induced cell apoptosis and endothelial-mesenchymal transition in human microvascular endothelial cells. Taken together, our results provide an alternative to MSCs-derived EVs–based PH therapy via using ReNcell as a reliable source of EVs. Particularly, our CAR-conjugated EVs may serve as a novel drug carrier that enhances the specificity and efficiency of drug delivery for effective PH-targeted therapy.

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