CoolMPS: evaluation of antibody labeling based massively parallel non-coding RNA sequencing

Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging

Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.

An observational human study investigating the effect of anabolic androgenic steroid use on the transcriptome of skeletal muscle and whole blood using RNA-Seq

BACKGROUND

The effects of Anabolic Androgenic Steroids (AAS) are largely illustrated through Androgen Receptor induced gene transcription, yet RNA-Seq has yet to be conducted on human whole blood and skeletal muscle. Investigating the transcriptional signature of AAS in blood may aid AAS detection and in muscle further understanding of AAS induced hypertrophy.

METHODS

Males aged 20-42 were recruited and sampled once: sedentary controls (C), resistance trained lifters (RT) and resistance trained current AAS users (RT-AS) who ceased exposure ≤ 2 or ≥ 10 weeks prior to sampling. RT-AS were sampled twice as Returning Participants (RP) if AAS usage ceased for ≥ 18 weeks. RNA was extracted from whole blood and trapezius muscle samples. RNA libraries were sequenced twice, for validation purposes, on the DNBSEQ-G400RS with either standard or CoolMPS PE100 reagents following MGI protocols. Genes were considered differentially expressed with FDR < 0.05 and a 1.2- fold change.

RESULTS

Cross-comparison of both standard reagent whole blood (N = 55: C = 7, RT = 20, RT-AS ≤ 2 = 14, RT-AS ≥ 10 = 10, RP = 4; N = 46: C = 6, RT = 17, RT-AS ≤ 2 = 12, RT-AS ≥ 10 = 8, RP = 3) sequencing datasets, showed that no genes or gene sets/pathways were differentially expressed between time points for RP or between group comparisons of RT-AS ≤ 2 vs. C, RT, or RT-AS ≥ 10. Cross-comparison of both muscle (N = 51, C = 5, RT = 17, RT-AS ≤ 2 = 15, RT-AS ≥ 10 = 11, RP = 3) sequencing (one standard & one CoolMPS reagent) datasets, showed one gene, CHRDL1, which has atrophying potential, was upregulated in RP visit two. In both muscle sequencing datasets, nine differentially expressed genes, overlapped with RT-AS ≤ 2 vs. RT and RT-AS ≤ 2 vs. C, but were not differentially expressed with RT vs. C, possibly suggesting they are from acute doping alone. No genes seemed to be differentially expressed in muscle after the long-term cessation of AAS, whereas a previous study found long term proteomic changes.

CONCLUSION

A whole blood transcriptional signature of AAS doping was not identified. However, RNA-Seq of muscle has identified numerous differentially expressed genes with known impacts on hypertrophic processes that may further our understanding on AAS induced hypertrophy. Differences in training regimens in participant groupings may have influenced results. Future studies should focus on longitudinal sampling pre, during and post-AAS exposure to better control for confounding variables.

Ageing-associated small RNA cargo of extracellular vesicles

Previous work on murine models and humans demonstrated global as well as tissue-specific molecular ageing trajectories of RNAs. Extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different tissues. We sequenced small regulatory RNAs (sncRNAs) in two mouse plasma fractions at five time points across the lifespan from 2-18 months: (1) sncRNAs that are free-circulating (fc-RNA) and (2) sncRNAs bound outside or inside EVs (EV-RNA). Different sncRNA classes exhibit unique ageing patterns that vary between the fcRNA and EV-RNA fractions. While tRNAs showed the highest correlation with ageing in both fractions, rRNAs exhibited inverse correlation trajectories between the EV- and fc-fractions. For miRNAs, the EV-RNA fraction was exceptionally strongly associated with ageing, especially the miR-29 family in adipose tissues. Sequencing of sncRNAs and coding genes in fat tissue of an independent cohort of aged mice up to 27 months highlighted the pivotal role of miR-29a-3p and miR-29b-3p in ageing-related gene regulation that we validated in a third cohort by RT-qPCR.

miRNATissueAtlas2: an update to the human miRNA tissue atlas

Small non-coding RNAs (sncRNAs) are pervasive regulators of physiological and pathological processes. We previously developed the human miRNA Tissue Atlas, detailing the expression of miRNAs across organs in the human body. Here, we present an updated resource containing sequencing data of 188 tissue samples comprising 21 organ types retrieved from six humans. Sampling the organs from the same bodies minimizes intra-individual variability and facilitates the making of a precise high-resolution body map of the non-coding transcriptome. The data allow shedding light on the organ- and organ system-specificity of piwi-interacting RNAs (piRNAs), transfer RNAs (tRNAs), microRNAs (miRNAs) and other non-coding RNAs. As use case of our resource, we describe the identification of highly specific ncRNAs in different organs. The update also contains 58 samples from six tissues of the Tabula Muris collection, allowing to check if the tissue specificity is evolutionary conserved between Homo sapiens and Mus musculus. The updated resource of 87 252 non-coding RNAs from nine non-coding RNA classes for all organs and organ systems is available online without any restrictions (https://www.ccb.uni-saarland.de/tissueatlas2).

miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale

Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.