Optimized single-nucleus transcriptional profiling by combinatorial indexing.

Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2-3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a 'Tiny-Sci' protocol for experiments in which input material is very limited.

EZH2 modulates retinoic acid signaling to ensure myotube formation during development.

Sequential differentiation of presomitic progenitors into myocytes and subsequently into myotubes and myofibers is essential for the myogenic differentiation program (MDP) crucial for muscle development. Signaling factors involved in MDP are polycomb repressive complex 2 (PRC2) targets in various developmental contexts. PRC2 is active in the developing myotomes during MDP, but how it regulates MDP is unclear. Here, we found that myocyte differentiation to myotubes requires Enhancer of Zeste 2 (EZH2), the catalytic component of PRC2. We observed elevated retinoic acid (RA) signaling in the prospective myocytes in the Ezh2 mutants (E8.5-MusEzh2 ), and its inhibition can partially rescue the myocyte differentiation defect. Together, our data demonstrate a new role for PRC2-EZH2 during myocyte differentiation into myotubes by modulating RA signaling.

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma.

Tumor Treating Fields (TTFields) therapy is an approved treatment that has known clinical efficacy against recurrent and newly diagnosed glioblastoma. However, the distribution of the electric fields and the corresponding pattern of energy deposition in the brain are poorly understood. To evaluate the physical parameters that may influence TTFields, postacquisition MP-RAGE, T1 and T2 MRI sequences from a responder with a right parietal glioblastoma were anatomically segmented and then solved using finite-element method to determine the distribution of the electric fields and rate of energy deposition at the gross tumor volume (GTV) and other intracranial structures. Electric field-volume histograms (EVH) and specific absorption rate-volume histograms (SARVH) were constructed to numerically evaluate the relative and/or absolute magnitude volumetric differences between models. The electric field parameters EAUC , VE150 , E95% , E50% , and E20% , as well as the SAR parameters SARAUC , VSAR7.5 , SAR95% , SAR50% , and SAR20% , facilitated comparisons between models derived from various conditions. Specifically, TTFields at the GTV were influenced by the dielectric characteristics of the adjacent tissues as well as the GTV itself, particularly the presence or absence of a necrotic core. The thickness of the cerebrospinal fluid on the convexity of the brain and the geometry of the tumor were also relevant factors. Finally, the position of the arrays also influenced the electric field distribution and rate of energy deposition in the GTV. Using EVH and SARVH, a personalized approach for TTFields treatment can be developed when various patient-related and tumor-related factors are incorporated into the planning procedure.