Highly Sensitive Serum Protein Analysis Using Magnetic Bead-Based Proximity Extension Assay.

Many protein biomarkers are present in biofluids at a very low level but may play critical roles in important biological processes. The fact that these low-abundance proteins remain largely unexplored underscores the importance of developing new tools for highly sensitive protein detection. Although digital enzyme-linked immunosorbent assay (ELISA) has demonstrated ultrahigh sensitivity compared with conventional ELISA, the requirement of specialized instruments limits the accessibility and prevents the widespread implementation. On the other hand, proximity ligation assays (PLA) and proximity extension assays (PEA) show sensitive and specific protein detection using regular laboratory setups, but their sensitivity needs to be further improved to match digital ELISA. To achieve highly sensitive protein detection with minimal accessibility limitation, we develop a magnetic bead-based PEA (magPEA), which posts triple epitope recognition requirement and enables extensive washing for improved sensitivity and enhanced specificity. We demonstrate that the incorporation of magnetic beads into PEA workflow facilitates orders of magnitude sensitivity improvement compared with conventional ELISA, homogeneous PEA, and solid-phase PLA and achieves limits of detection close to that of digital ELISA when using IL-6, IL-8, and GM-CSF as validation. Our magPEA provides a simple approach for highly sensitive protein detection that can be readily implemented to other laboratories and will thus ultimately accelerate the study of the low abundance protein biomarkers in the future.

Combining DNA and protein alignments to improve genome annotation with LiftOn.

As the number and variety of assembled genomes continues to grow, the number of annotated genomes is falling behind, particularly for eukaryotes. DNA-based mapping tools help to address this challenge, but they are only able to transfer annotation between closely-related species. Here we introduce LiftOn, a homology-based software tool that integrates DNA and protein alignments to enhance the accuracy of genome-scale annotation and to allow mapping between relatively distant species. LiftOn's protein-centric algorithm considers both types of alignments, chooses optimal open reading frames, resolves overlapping gene loci, and finds additional gene copies where they exist. LiftOn can reliably transfer annotation between genomes representing members of the same species, as we demonstrate on human, mouse, honey bee, rice, and Arabidopsis thaliana. It can further map annotation effectively across species pairs as far apart as mouse and rat or Drosophila melanogaster and D. erecta.

A do it yourself (DIY) point-of-care wrist ultrasound phantom for joint access training.

BACKGROUND: Joint access is essential for arthrocentesis, or joint aspiration of fluids. Joint treatments that are not performed properly can result in avoidable patient issues such as damage to the muscles, tendons, and blood vessels surrounding the joint. The use of ultrasound has become the gold standard for this procedure and proven to be a support in the skill learning process. However, success with this equipment, particularly in small joints like the wrist, depends on a clinician's capacity to recognize the crucial landmarks that guide these procedures. Prior to executing on a real patient, task trainers have proven to be an effective way for doctors to practice and prepare for procedures. However, shortcomings of current solutions include high purchase costs, incompatibility with ultrasound imaging, and low reusability. In addition, since this is a procedure that is not performed frequently, there may not be space or resources available in healthcare facilities to accommodate one at the point of care. This study aimed to close the existing gap by developing a DIY ultrasound compatible task trainer for wrist joint access training. RESULTS: We developed a novel ultrasound compatible wrist joint model that can be made from sustainable materials and reusable parts, thus reducing the costs for acquisition and environmental impact. Our model, which was produced utilizing small-batch production methods, is made up of 3D-printed bones enclosed in an ultrasound-compatible gelatin mixture. It can be easily remade after each practice session, removing needle tracks that are visible under ultrasound for conventional phantoms. The ultrasonic properties of this model were tested through pixel brightness analysis and visual inspection of simulated anatomical structures. CONCLUSION: Our results report the advantages and limitations of the proposed model regarding production, practice, and ultrasound compatibility. While future work entails the transfer to patients of the same skill, this reusable and replicable model has proven, when presented to experts, to be successful in representing the physical characteristics and ultrasound profile of significant anatomical structures. This novel DIY product could be an effective alternative to teach procedures in the context of resource-restrained clinical simulation centers.

Splam: a deep-learning-based splice site predictor that improves spliced alignments.

The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. Here we describe Splam, a novel method for predicting splice junctions in DNA based on deep residual convolutional neural networks. Unlike some previous models, Splam looks at a relatively limited window of 400 base pairs flanking each splice site, motivated by the observation that the biological process of splicing relies primarily on signals within this window. Additionally, Splam introduces the idea of training the network on donor and acceptor pairs together, based on the principle that the splicing machinery recognizes both ends of each intron at once. We compare Splam's accuracy to recent state-of-the-art splice site prediction methods, particularly SpliceAI, another method that uses deep neural networks. Our results show that Splam is consistently more accurate than SpliceAI, with an overall accuracy of 96% at predicting human splice junctions. Splam generalizes even to non-human species, including distant ones like the flowering plant Arabidopsis thaliana. Finally, we demonstrate the use of Splam on a novel application: processing the spliced alignments of RNA-seq data to identify and eliminate errors. We show that when used in this manner, Splam yields substantial improvements in the accuracy of downstream transcriptome analysis of both poly(A) and ribo-depleted RNA-seq libraries. Overall, Splam offers a faster and more accurate approach to detecting splice junctions, while also providing a reliable and efficient solution for cleaning up erroneous spliced alignments.