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Human pluripotent stem cells (hPSCs) offer an unprecedented opportunity to model diverse cell types and tissues. To enable systematic exploration of the programming landscape mediated by transcription factors (TFs), we present the Human TFome, a comprehensive library containing 1,564 TF genes and 1,732 TF splice-isoforms. By screening the library in three hPSC lines, we discovered 290 TFs, including 241 previously unreported, that induce differentiation in four days without alteration of external soluble or biomechanical cues. We used four of the hits to program hPSCs into neurons, fibroblasts, oligodendrocytes and vascular endothelial–like cells that have molecular and functional similarity to primary cells. Our cell-autonomous approach enabled parallel programming of hPSCs into multiple cell types simultaneously. We also demonstrated orthogonal programming by including oligodendrocyte-inducible hPSCs with unmodified hPSCs to generate cerebral organoids, which expedited in situ myelination. Large-scale combinatorial screening of the Human TFome will complement other strategies for cell engineering based on developmental biology and computational systems biology.

The health and function of our visual system relies on accurate gene expression. While many genetic mutations are associated with visual impairment and blindness, we are just beginning to understand the complex interplay between gene regulation and retinal pathologies. MicroRNAs (miRNAs), a class of non-coding RNAs, are important regulators of gene expression that exert their function through post-transcriptional silencing of complementary mRNA targets. According to recent transcriptomic analyses, certain miRNA species are expressed in all retinal cell types, while others are cell type-specific. As miRNAs play important roles in homeostasis, cellular function, and survival of differentiated retinal cell types, their dysregulation is associated with retinal degenerative diseases. Thus, advancing our understanding of the genetic networks modulated by miRNAs is central to harnessing their potential as therapeutic agents to overcome visual impairment. In this review, we summarize the role of distinct miRNAs in specific retinal cell types, the current knowledge on their implication in inherited retinal disorders, and their potential as therapeutic agents.

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Retinal hypoxia triggers abnormal vessel growth and microvascular hyper‐permeability in ischemic retinopathies. Whereas vascular endothelial growth factor A (VEGF‐A) inhibitors significantly hinder disease progression, their benefits to retinal neurons remain poorly understood. Similar to humans, oxygen‐induced retinopathy (OIR) mice exhibit severe retinal microvascular malformations and profound neuronal dysfunction. OIR mice are thus a phenocopy of human retinopathy of prematurity, and a proxy for investigating advanced stages of proliferative diabetic retinopathy. Hence, the OIR model offers an excellent platform for assessing morpho‐functional responses of the ischemic retina to anti‐angiogenic therapies. Using this model, we investigated the retinal responses to VEGF‐Trap (Aflibercept), an anti‐angiogenic agent recognizing ligands of VEGF receptors 1 and 2 that possesses regulatory approval for the treatment of neovascular age‐related macular degeneration, macular edema secondary to retinal vein occlusion and diabetic macular edema. Our results indicate that Aflibercept not only reduces the severity of retinal microvascular aberrations but also significantly improves neuroretinal function. Aflibercept administration significantly enhanced light‐responsiveness, as revealed by electroretinographic examinations, and led to increased numbers of dopaminergic amacrine cells. Additionally, retinal transcriptional profiling revealed the concerted regulation of both angiogenic and neuronal targets, including transcripts encoding subunits of transmitter receptors relevant to amacrine cell function. Thus, Aflibercept represents a promising therapeutic alternative for the treatment of further progressive ischemic retinal neurovasculopathies beyond the set of disease conditions for which it has regulatory approval. Cover Image for this issue: doi: .

Abstract. In many applications (like social or sensor networks) the information generated can be represented as a continuous stream of RDF items, where each item describes an application event (social network post, sensor measurement, etc). In this paper we focus on compressing RDF streams. In particular, we propose an approach for lossless RDF stream compression, named RDSZ (RDF Differential Stream compressor based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding mechanism with the general purpose stream compressor Zlib. Empirical evaluation using several RDF stream datasets shows that this combination produces gains in compression ratios with respect to using Zlib alone.

MicroRNAs (miRNAs) are small RNA molecules with important roles in post-transcriptional regulation of gene expression. In recent years, the predicted number of miRNAs has skyrocketed, largely as a consequence of high-throughput sequencing technologies becoming ubiquitous. This dramatic increase in miRNA candidates poses multiple challenges in terms of data deposition, curation, and validation. Although multiple databases containing miRNA annotations and targets have been developed, ensuring data quality by validating miRNA-target interactions requires the efforts of the research community. In order to generate databases containing biologically active miRNAs, it is imperative to overcome a multitude of hurdles, including restricted miRNA expression patterns, distinct miRNA biogenesis machineries, and divergent miRNA-mRNA interaction dynamics. In the present review, we discuss recent advances and limitations in miRNA prediction, identification, and validation. Lastly, we focus on the most enriched neuronal miRNA, miR-124, and its gene regulatory network in human neurons, which has been revealed using a combined computational and experimental approach.

Due to the multiple applications of random numbers in computer systems (cryptography, online gambling, computer simulation, etc.) it is important to have mechanisms to generate these numbers. True Random Number Generators (TRNGs) are commonly used for this purpose. TRNGs rely on non-deterministic sources to generate randomness. Physical processes (like noise in semiconductors, quantum phenomenon, etc.) play this role in state of the art TRNGs. In this paper, we depart from previous work and explore the possibility of defining social TRNGs using the stream of public messages of the microblogging service Twitter as randomness source. Thus, we define two TRNGs based on Twitter stream information and evaluate them using the National Institute of Standards and Technology (NIST) statistical test suite. The results of the evaluation confirm the feasibility of the proposed approach.