Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development.

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.

Multibarrier-penetrating drug delivery systems for deep tumor therapy based on synergistic penetration strategy.

Nanotherapies, valued for their high efficacy and low toxicity, frequently serve as antitumor treatments, but do not readily penetrate deep into tumor tissues and cells. Here we developed an improved tumor-penetrating peptide (TPP)-based drug delivery system. Briefly, the established TPP iNGR was modified to generate a linear NGR peptide capable of transporting nanotherapeutic drugs into tumors through a CendR pathway-dependent, neuropilin-1 receptor-mediated process. Although TPPs have been reported to reach intended tumor targets, they often fail to penetrate cell membranes to deliver tumoricidal drugs to intracellular targets. We addressed this issue by harnessing cell penetrating peptide technology to develop a liposome-based multibarrier-penetrating delivery system (mbPDS) with improved synergistic drug penetration into deep tumor tissues and cells. The system incorporated doxorubicin-loaded liposomes coated with nona-arginine (R9) CPP and cyclic iNGR (CRNGRGPDC) molecules, yielding Lip-mbPDS. Lip-mbPDS tumor-targeting, tumor cell/tissue-penetrating and antitumor capabilities were assessed using CD13-positive human fibrosarcoma-derived cell (HT1080)-based in vitro and in vivo tumor models. Lip-mbPDS evaluation included three-dimensional layer-by-layer confocal laser scanning microscopy, cell internalization/toxicity assays, three-dimensional tumor spheroid-based penetration assays and antitumor efficacy assays conducted in an animal model. Lip-mbPDS provided enhanced synergistic drug penetration of multiple biointerfaces for potentially deep tumor therapeutic outcomes.

Petascale pipeline for precise alignment of images from serial section electron microscopy.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Special nuclear layer contacts between starburst amacrine cells in the mouse retina.

Starburst amacrine cells are a prominent neuron type in the mammalian retina that has been well-studied for its role in direction-selective information processing. One specific property of these cells is that their dendrites tightly stratify at specific depths within the inner plexiform layer (IPL), which, together with their unique expression of choline acetyltransferase (ChAT), has made them the most common depth marker for studying other retinal neurons in the IPL. This stratifying property makes it unexpected that they could routinely have dendrites reaching into the nuclear layer or that they could have somatic contact specializations, which is exactly what we have found in this study. Specifically, an electron microscopic image volume of sufficient size from a mouse retina provided us with the opportunity to anatomically observe both microscopic details and collective patterns, and our detailed cell reconstructions revealed interesting cell-cell contacts between starburst amacrine neurons. The contact characteristics differ between the respective On and Off starburst amacrine subpopulations, but both occur within the soma layers, as opposed to their regular contact laminae within the inner plexiform layer.