sRNAbench and sRNAtoolbox 2022 update: accurate miRNA and sncRNA profiling for model and non-model organisms

The NCBI Sequence Read Archive currently hosts microRNA sequencing data for over 800 different species, evidencing the existence of a broad taxonomic distribution in the field of small RNA research. Simultaneously, the number of samples per miRNA-seq study continues to increase resulting in a vast amount of data that requires accurate, fast and user-friendly analysis methods. Since the previous release of sRNAtoolbox in 2019, 55 000 sRNAbench jobs have been submitted which has motivated many improvements in its usability and the scope of the underlying annotation database. With this update, users can upload an unlimited number of samples or import them from Google Drive, Dropbox or URLs. Micro- and small RNA profiling can now be carried out using high-confidence Metazoan and plant specific databases, MirGeneDB and PmiREN respectively, together with genome assemblies and libraries from 441 Ensembl species. The new results page includes straightforward sample annotation to allow downstream differential expression analysis with sRNAde. Unassigned reads can also be explored by means of a new tool that performs mapping to microbial references, which can reveal contamination events or biologically meaningful findings as we describe in the example. sRNAtoolbox is available at: https://arn.ugr.es/srnatoolbox/</a

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000

NORMSEQ: a tool for evaluation, selection and visualization of RNA-Seq normalization methods

RNA-sequencing has become one of the most used high-throughput approaches to gain knowledge about the expression of all different RNA subpopulations. However, technical artifacts, either introduced during library preparation and/or data analysis, can influence the detected RNA expression levels. A critical step, especially in large and low input datasets or studies, is data normalization, which aims at eliminating the variability in data that is not related to biology. Many normalization methods have been developed, each of them relying on different assumptions, making the selection of the appropriate normalization strategy key to preserve biological information. To address this, we developed NormSeq, a free web-server tool to systematically assess the performance of normalization methods in a given dataset. A key feature of NormSeq is the implementation of information gain to guide the selection of the best normalization method, which is crucial to eliminate or at least reduce non-biological variability. Altogether, NormSeq provides an easy-to-use platform to explore different aspects of gene expression data with a special focus on data normalization to help researchers, even without bioinformatics expertise, to obtain reliable biological inference from their data. NormSeq is freely available at: https://arn.ugr.es/normSeq

Example of HRUS image.

<p>HRUS is able to provide specific anatomical information of bladder tumor development in a 3-dimensional plane. The tumor size was assessed based on the ROIs drawn around the tumor borders for every slice of 125 μm. Tumor is marked in blue.</p

Histopathology and immunohistochemistry.

<p>(A) HE images confirm imaging results and show tumor growth extending from the bladder wall in to the bladder lumen. Tumor bearing mice show (B) positive Ki-67 staining, (C) positive PECAM staining as well as (D) high TRAIL expression.</p

BLI, HRUS and PAI images taken at day 17 post-injection.

<p>(A) BLI displays intense luminescence emission (left image), HRUS shows a tumor volume of 102.6 mm³(middle image), PAI demonstrates an oxygenated tumor center (right image). Oxygen saturation levels ranging from 0% (blue) to 100% (red). An absence of signal is displayed by black pixels. (B) Discordant results between BLI/PAI and HRUS, demonstrating aberrant growth pattern.</p

A Multimodal Imaging Approach for Longitudinal Evaluation of Bladder Tumor Development in an Orthotopic Murine Model

<div><p>Bladder cancer is the fourth most common malignancy amongst men in Western industrialized countries with an initial response rate of 70% for the non-muscle invasive type, and improving therapy efficacy is highly needed. For this, an appropriate, reliable animal model is essential to gain insight into mechanisms of tumor growth for use in response monitoring of (new) agents. Several animal models have been described in previous studies, but so far success has been hampered due to the absence of imaging methods to follow tumor growth non-invasively over time. Recent developments of multimodal imaging methods for use in animal research have substantially strengthened these options of <i>in vivo</i> visualization of tumor growth. In the present study, a multimodal imaging approach was addressed to investigate bladder tumor proliferation longitudinally. The complementary abilities of Bioluminescence, High Resolution Ultrasound and Photo-acoustic Imaging permit a better understanding of bladder tumor development. Hybrid imaging modalities allow the integration of individual strengths to enable sensitive and improved quantification and understanding of tumor biology, and ultimately, can aid in the discovery and development of new therapeutics.</p></div

Longitudinal multimodal imaging assessment of <i>in vivo</i> bladder tumor growth.

<p>(A) BLI acquisition of MB49-luc tumor-bearing C57Bl/6 mice during local injury method 18 minutes after s.c. luciferin injection. BLI is expressed as the number of photons/sec; the graph is representing the increase in bioluminescence activity over time (n = 4, mean ± SD). (B) HRUS results expressed as tumor volume in mm³ over time after tumor cell implantation (n = 4, mean ± SD). (C) Serial PAI measurements showing average sO₂ values in tumor bearing mice (n = 4, mean ± SD).</p

Multimodal imaging assessment during the development of our local injury method of tumor cell implantation.

<p>(A) BLI acquisition of MB49-luc tumor-bearing C57Bl/6 mice during local injury method 18 minutes after s.c. luciferin injection. BLI is expressed as the number of photons/sec; the graph is representing the increase in bioluminescence activity over time (n = 7, mean ± SD). Difference in bioluminescence emission is minimal between both groups. (B) HRUS results expressed as tumor volume in mm3 over time after tumor cell implantation (n = 7, mean ± SD). High (n = 2) and low (n = 5) tumor growth kinetics could be observed, thereby distinguishing two growth patterns of tumor formation. (C) Serial PAI measurements showing average sO₂ values in tumor bearing mice. Similar trends in sO₂ values can be observed in animals with high (n = 2) and low (n = 5) tumor growth kinetics, though a more pronounced drop in sO₂ levels was observed in animals with high tumor growth kinetics.</p

Visualization of the scratching procedure with HRUS.

<p>HRUS images were obtained during the optimized local injury method in a sham mouse. Marked in blue and red is the scratching blunted needle and the bladder wall, respectively. (A) scratching of the bladder wall for 1 minute, (B) bladder immediately after the procedure, (C) bladder region after 10 days.</p