Standard Flow Multiplexed Proteomics (SFloMPro)—An Accessible Alternative to NanoFlow Based Shotgun Proteomics

Multiplexed proteomics using isobaric tagging allows for simultaneously comparing the proteomes of multiple samples. In this technique, digested peptides from each sample are labeled with a chemical tag prior to pooling sample for LC-MS/MS with nanoflow chromatography (NanoLC). The isobaric nature of the tag prevents deconvolution of samples until fragmentation liberates the isotopically labeled reporter ions. To ensure efficient peptide labeling, large concentrations of labeling reagents are included in the reagent kits to allow scientists to use high ratios of chemical label per peptide. The increasing speed and sensitivity of mass spectrometers has reduced the peptide concentration required for analysis, leading to most of the label or labeled sample to be discarded. In conjunction, improvements in the speed of sample loading, reliable pump pressure, and stable gradient construction of analytical flow HPLCs has continued to improve the sample delivery process to the mass spectrometer. In this study we describe a method for performing multiplexed proteomics without the use of NanoLC by using offline fractionation of labeled peptides followed by rapid “standard flow” HPLC gradient LC-MS/MS. Standard Flow Multiplexed Proteomics (SFloMPro) enables high coverage quantitative proteomics of up to 16 mammalian samples in about 24 h. In this study, we compare NanoLC and SFloMPro analysis of fractionated samples. Our results demonstrate that comparable data is obtained by injecting 20 µg of labeled peptides per fraction with SFloMPro, compared to 1 µg per fraction with NanoLC. We conclude that, for experiments where protein concentration is not strictly limited, SFloMPro is a competitive approach to traditional NanoLC workflows with improved up-time, reliability and at a lower relative cost per sample.

Time Series Prediction of Viable Embryo and Automatic Grading in IVF using Deep Learning

Background: The process of In Vitro Fertilization (IVF) involves collecting multiple samples of mature eggs that are fertilized with sperms in the IVF laboratory. They are eventually graded, and the most viable embryo out of all the samples is selected for transfer in the mother’s womb for a healthy pregnancy. Currently, the process of grading and selecting the healthiest embryo is performed by visual morphology, and manual records are maintained by embryologists. Objectives: Maintaining manual records makes the process very tedious, time-consuming, and error-prone. The absence of a universal grading leads to high subjectivity and low success rate of pregnancy. To improve the chances of pregnancy, multiple embryos are transferred in the womb elevating the risk of multiple pregnancies. In this paper, we propose a deep learning-based method to perform the automatic grading of the embryos using time series prediction with Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). Methods: CNN extracts the features of the images of embryos, and a sequence of such features is fed to LSTM for time series prediction, which gives the final grade. Results: Our model gave an ideal accuracy of 100% on training and validation. A comparison of obtained results is made with those obtained from a GRU model as well as other pre-trained models. Conclusion: The automated process is robust and eliminates subjectivity. The days-long hard work can now be replaced with our model, which gives the grading within 8 seconds with a GPU.

Multiple Cross Displacement Amplification Combined With Real-Time Polymerase Chain Reaction Platform: A Rapid, Sensitive Method to Detect Mycobacterium tuberculosis

In this study, we evaluated the diagnostic accuracy of multiple cross displacement amplification (MCDA) combined with real-time PCR platform in pulmonary tuberculosis (PTB) patients. Total 228 PTB patients and 141 non-TB cases were enrolled. Based on the analysis of the first available sample of all participants, MCDA assay showed a higher overall sensitivity (64.0%), with a difference of more than 10% compared with Xpert MTB/RIF (Xpert) assay (51.8%, P < 0.05) and combined liquid and solid culture (47.8%, P < 0.001) for PTB diagnosis. In particular, MCDA assay detected 31 probable TB patients, which notably increased the percentage of confirmed TB from 57.9% (132/228) to 71.5% (163/228). The specificities of microscopy, culture, Xpert and MCDA assay were 100% (141/141), 100% (141/141), 100% (141/141), and 98.6% (139/141), respectively. Among the patients with multiple samples, per patient sensitivity of MCDA assay was 60.5% (52/86) when only the first available sputum sample was taken into account, and the sensitivity increased to 75.6% (65/86) when all samples tested by MCDA assay were included into the analysis. Therefore, MCDA assay established in this study is rapid, accurate and affordable, which has the potential in assisting the accurate and rapid diagnosis of PTB and speed up initiation of TB treatment in settings equipped with real-time PCR platform.

High-Throughput Strategy for Profiling Sequential Section With Multiplex Staining of Mouse Brain

The brain modulates specific functions in its various regions. Understanding the organization of different cells in the whole brain is crucial for investigating brain functions. Previous studies have focused on several regions and have had difficulty analyzing serial tissue samples. In this study, we introduced a pipeline to acquire anatomical and histological information quickly and efficiently from serial sections. First, we developed a serial brain-slice-staining method to stain serial sections and obtained more than 98.5% of slices with high integrity. Subsequently, using the self-developed analysis software, we registered and quantified the signals of imaged sections to the Allen Mouse Brain Common Coordinate Framework, which is compatible with multimodal images and slant section planes. Finally, we validated the pipeline with immunostaining by analyzing the activity variance in the whole brain during acute stress in aging and young mice. By removing the problems resulting from repeated manual operations, this pipeline is widely applicable to serial brain slices from multiple samples in a rapid and convenient manner, which benefits to facilitate research in life sciences.

isoformant: A visual toolkit for reference-free long-read isoform analysis at single-read resolution

isoformant is an analytical toolkit for isoform characterization of Oxford Nanopore Technologies (ONT) long-transcript sequencing data (i.e. direct RNA and cDNA). Deployment of these tools using Jupyter Notebook enables interactive analysis of user- defined region-of-interest (ROI), typically a gene. The core module of isoformant clus- ters sequencing reads by k-mer density to generate isoform consensus sequences without the requirement for a reference genome or prior annotations. The inclusion of differential isoform usage hypothesis testing based on read distribution among clusters enables com- parison across multiple samples. Here, as proof-of-principle, we demonstrate the utility of isoformant for analyzing isoform diversity of commercially-available isoform standard mixtures. isoformant is available here: https://github.com/danledinh/isoformant.

REVOLVER: a low-cost automated protein purifier based on parallel preparative gravity column workflows

Protein purification is a ubiquitous operation in biochemistry and life sciences and represents a key step to producing purified proteins for research (understanding how proteins work) and various applications. The need for scalable and parallel protein purification systems keeps growing due to the increase in throughput in the production of recombinant proteins and in the ever-growing scale of biochemistry research. Therefore, automating the process to handle multiple samples in parallel with minimal human intervention is highly desirable; yet only a handful of such tools have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable and automatic protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER completes a full protein purification process with almost no human intervention and yields results equivalent to those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems will be useful to accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering.

Real-time Optimization and Implementation of Multi-rate Digital FIR Kernel Filter

One ideal performance of this design is in the areas of decimation where a decimation factor of 10, 45-order and pass band ripple of 1dB and interpolation of sampled rates where a sinusoidal signal input produced a ripple free output with interpolation factor of 10, 52-order and stopband attenuation of 60dB. Owing to the multiple samples of filter length of 200, the filter performed down sampling preceded with filtering as well as up sampling preceded with filtering, hence multi-rate filter by allowing a low threshold of frequency of to be passed, blocking a high threshold of and vice versa. There was resampled output increased to 150% preceded by filtering. The filter coefficients for low pass and high pass Digital FIR filter, through the least square regression method, parks McClellan Algorithm and window methods were employed for easy optimization. More so, there was creation of 2-4-5 filter channel banks through the 2nd-level convolution of their down sampling and up sampling filtering techniques during the multi-irate filtering to ensure the design of error-free Digital FIR Filter using MATLAB File editor(M-File) and tool boxes for writing the C-programming of the design. In the analysis, the mean and standard deviation of the low pass Digital FIR Filter output during decimation and interpolation are (0.26, 6.13) and (0.004,1.22) respectively.

RPPUF: An Ultra-Lightweight Reconfigurable Pico-Physically Unclonable Function for Resource-Constrained IoT Devices

With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, conventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a hardware-based, low-cost alternative solution to provide security for IoT devices. It utilizes the inherent nature of hardware to generate a random and unpredictable fingerprint to uniquely identify an IoT device. However, despite existing PUFs having exhibited a good performance, they are not suitable for effective application on resource-constrained IoT devices due to the limited number of challenge-response pairs (CRPs) generated per unit area and the large hardware resources overhead. To solve these problems, this article presents an ultra-lightweight reconfigurable PUF solution, which is named RPPUF. Our method is built on pico-PUF (PPUF). By incorporating configurable logics, one single RPPUF can be instantiated into multiple samples through configurable information K. We implement and verify our design on the Xilinx Spartan-6 field programmable gate array (FPGA) microboards. The experimental results demonstrate that, compared to previous work, our method increases the uniqueness, reliability and uniformity by up to 4.13%, 16.98% and 10.5%, respectively, while dramatically reducing the hardware resource overhead by 98.16% when a 128-bit PUF response is generated. Moreover, the bit per cost (BPC) metric of our proposed RPPUF increased by up to 28.5 and 53.37 times than that of PPUF and the improved butterfly PUF, respectively. This confirms that the proposed RPPUF is ultra-lightweight with a good performance, making it more appropriate and efficient to apply in FPGA-based IoT devices with constrained resources.

A Bioinformatic Workflow for InDel Analysis in the Wheat Multi-Copy α-Gliadin Gene Family Engineered with CRISPR/Cas9

The α-gliadins of wheat, along with other gluten components, are responsible for bread viscoelastic properties. However, they are also related to human pathologies as celiac disease or non-celiac wheat sensitivity. CRISPR/Cas was successfully used to knockout α-gliadin genes in bread and durum wheat, therefore, obtaining low gluten wheat lines. Nevertheless, the mutation analysis of these genes is complex as they present multiple and high homology copies arranged in tandem in A, B, and D subgenomes. In this work, we present a bioinformatic pipeline based on NGS amplicon sequencing for the analysis of insertions and deletions (InDels) in α-gliadin genes targeted with two single guides RNA (sgRNA). This approach allows the identification of mutated amplicons and the analysis of InDels through comparison to the most similar wild type parental sequence. TMM normalization was performed for inter-sample comparisons; being able to study the abundance of each InDel throughout generations and observe the effects of the segregation of Cas9 coding sequence in different lines. The usefulness of the workflow is relevant to identify possible genomic rearrangements such as large deletions due to Cas9 cleavage activity. This pipeline enables a fast characterization of mutations in multiple samples for a multi-copy gene family.