MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics

Transcriptional profiling of HERV-K(HML-2) in amyotrophic lateral sclerosis and potential implications for expression of HML-2 proteins

Abstract Background Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder. About 90% of ALS cases are without a known genetic cause. The human endogenous retrovirus multi-copy HERV-K(HML-2) group was recently reported to potentially contribute to neurodegeneration and disease pathogenesis in ALS because of transcriptional upregulation and toxic effects of HML-2 Envelope (Env) protein. Env and other proteins are encoded by some transcriptionally active HML-2 loci. However, more detailed information is required regarding which HML-2 loci are transcribed in ALS, which of their proteins are expressed, and differences between the disease and non-disease states. Methods For brain and spinal cord tissue samples from ALS patients and controls, we identified transcribed HML-2 loci by generating and mapping HML-2-specific cDNA sequences. We predicted expression of HML-2 env gene-derived proteins based on the observed cDNA sequences. Furthermore, we determined overall HML-2 transcript levels by RT-qPCR and investigated presence of HML-2 Env protein in ALS and control tissue samples by Western blotting. Results We identified 24 different transcribed HML-2 loci. Some of those loci are transcribed at relatively high levels. However, significant differences in HML-2 loci transcriptional activities were not seen when comparing ALS and controls. Likewise, overall HML-2 transcript levels, as determined by RT-qPCR, were not significantly different between ALS and controls. Indeed, we were unable to detect full-length HML-2 Env protein in ALS and control tissue samples despite reasonable sensitivity. Rather our analyses suggest that a number of HML-2 protein variants other than full-length Env may potentially be expressed in ALS patients. Conclusions Our results expand and refine recent publications on HERV-K(HML-2) and ALS. Some of our results are in conflict with recent findings and call for further specific analyses. Our profiling of HML-2 transcription in ALS opens up the possibility that HML-2 proteins other than canonical full-length Env may have to be considered when studying the role of HML-2 in ALS disease

Perseus Metis Data

Metis is a plugin for the Perseus software for analysing quantitative multi-omics data based on metabolic pathways

What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics

Computational workflows for mass spectrometry-based shotgun proteomics and untargeted metabolomics share many steps. Despite the similarities, untargeted metabolomics is lagging behind in terms of reliable fully automated quantitative data analysis. We argue that metabolomics will strongly benefit from the adaptation of successful automated proteomics workflows to metabolomics. MaxQuant is a popular platform for proteomics data analysis and is widely considered to be superior in achieving high precursor mass accuracies through advanced nonlinear recalibration, usually leading to five to ten-fold better accuracy in complex LC-MS/MS runs. This translates to a sharp decrease in the number of peptide candidates per measured feature, thereby strongly improving the coverage of identified peptides. We argue that similar strategies can be applied to untargeted metabolomics, leading to equivalent improvements in metabolite identification

Perseus Metis Data

Metis is a plugin for the Perseus software for analysing quantitative multi-omics data based on metabolic pathways