Designing Randomized DNA Sequences Free of Restriction Enzyme Recognition Sites

A reinforcement learning framework for pooled oligonucleotide design

MOTIVATION

The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions.

RESULTS

We present a general reinforcement learning (RL) framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows 'black-box' design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: (i) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); (ii) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor) and (iii) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how RL offers a general solution for complex oligo design problems.

AVAILABILITY AND IMPLEMENTATION

OligoRL and all simulation codes are available as a Julia package at http://jensenlab.net/tools and archived at https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://github.com/bmdavid2/OligoRL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Microscopic examination of spatial transcriptome using Seq-Scope

Spatial barcoding technologies have the potential to reveal histological details of transcriptomic profiles; however, they are currently limited by their low resolution. Here, we report Seq-Scope, a spatial barcoding technology with a resolution comparable to an optical microscope. Seq-Scope is based on a solid-phase amplification of randomly barcoded single-molecule oligonucleotides using an Illumina sequencing platform. The resulting clusters annotated with spatial coordinates are processed to expose RNA-capture moiety. These RNA-capturing barcoded clusters define the pixels of Seq-Scope that are ∼0.5-0.8 μm apart from each other. From tissue sections, Seq-Scope visualizes spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation-fibrosis (injured liver) axes, cellular components including single-cell types and subtypes, and subcellular architectures of nucleus and cytoplasm. Seq-Scope is quick, straightforward, precise, and easy-to-implement and makes spatial single-cell analysis accessible to a wide group of biomedical researchers.

Electroactive composite scaffold with locally expressed osteoinductive factor for synergistic bone repair upon electrical stimulation

Tissue engineering is a promising strategy for the repair of large-scale bone defects, in which scaffolds and growth factors are two critical issues influencing the efficacy of bone regeneration. Unfortunately, the broad application of growth factors is limited by their poor stability in the scaffolds. In the present study, the strictly controlled expression of human bone morphogenetic protein-4 (hBMP-4) in the presence of doxycycline is achieved by adding an hBMP-4 gene fragment into a non-viral artificial restructuring plasmid vector (pSTAR) to form the pSTAR-hBMP-4 plasmid (phBMP-4). Furthermore, the controlled release of phBMP-4 is obtained with an electroactive tissue engineering scaffold, generated by combining a triblock copolymer of poly(l-lactic acid)-block-aniline pentamer-block-poly(l-lactic acid) (PLA-AP) with poly(lactic-co-glycolic acid)/hydroxyapatite (PLGA/HA). This PLGA/HA/PLA-AP/phBMP-4 composite scaffold, with controlled gene release and Dox-regulated gene expression upon electrical stimulation, operating synergistically, exhibits an improved cell proliferation ability, enhanced osteogenesis differentiation in vitro, and effective bone healing in vivo in a rabbit radial defect model. Taking these results together, the proposed smart PLGA/HA/PLA-AP/phBMP-4 scaffold lays a solid theoretical and experimental basis for future applications of such multi-functional materials in bone tissue engineering to help patients in need.