Biology subjects

Koch, H.

Publications and source records attributed to Koch, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

mRNA delivery of dimeric human IgA protects mucosal tissues from bacterial infection

Monoclonal antibody (mAb) therapy is a promising infectious disease intervention strategy but is limited to IgG1 isotypes that have restricted access to mucosal sites. IgA is well-established as the predominant antibody isotype in mucosal secretions but is clinically underutilized. To enable development of IgA-based mAbs, we exploited mRNA platform technology and demonstrated expression of functional, antigen-specific IgA (IgAmRNA) that can limit bacterial invasion in the intestine and prevent colonization in the lung. Moreover, in vivo IgAmRNA had enhanced serum half-life and a greater degree of sialylation than a recombinantly produced IgA. The results underscore the potential of mRNA-based platforms to deliver protective human mAbs to mucosal surfaces and open new avenues to combat infectious diseases in the face of pervasive antibiotic resistance. One Sentence SummarymRNA-encoded human monoclonal IgA traffics to mucosal tissues and provides protection against bacterial challenge

immunology↗

bioRxiv2021

Dravet variant SCN1AA1783V impairs interneuron firing predominantly by altered channel activation

Dravet syndrome (DS) is a developmental epileptic encephalopathy mainly caused by functional NaV1.1 haploinsufficiency in interneurons (IN). Recently, a new conditional mouse model expressing the recurrent human p.A1783V missense variant has become available. Here we provide an electrophysiological characterization of this variant in tsA201 cells, revealing both altered voltage-dependence of activation and slow inactivation without reduced sodium peak current density. Simulating IN excitability in a Hodgkin-Huxley one-compartment model suggested surprisingly similar firing deficits for Scn1aA1783V and full haploinsufficiency as caused by heterozygous truncation variants. Impaired NaVA1783V channel activation was predicted to have a significantly larger impact on channel function than altered slow inactivation and is therefore proposed as the main mechanism underlying IN dysfunction. The computational model was validated in cortical organotypic slice cultures derived from conditional Scn1aA1783V mice. Pan-neuronal activation of the p.A1783V variant in vitro confirmed the predicted IN firing deficit while demonstrating normal excitability of pyramidal neurons. Taken together these data demonstrate that despite maintained physiological peak currents density LOF gating properties may match effects of full haploinsufficiency on neuronal level, thereby causing DS. HighlightsNaV1.1A1783V alters voltage-dependence of activation and slow inactivation while not affecting fast inactivation. Depolarizing and hyperpolarizing shifts of activation and slow inactivation curves result in combined channel loss of function (LOF). Simulations of NaV1.1A1783V interneuronal properties indicate reduced action potential firing rates comparable to full SCN1A haploinsufficiency, which is often found in Dravet syndrome. In silico modelling identifies impaired channel activation as the predominant mechanism of channel LOF. Panneuronal induction of Scn1a+/A1783V in a cortical slice culture model confirms restriction of loss of function and its restriction to interneurons.

neuroscience↗

bioRxiv2021

Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospiras in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes. ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

microbiology↗

bioRxiv2021

Agri-environment nectar chemistry suppresses parasite social epidemiology in an important pollinator

Emergent infectious diseases are a principal driver of biodiversity loss globally. The population and range declines of a suite of North American bumblebees, a group of important pollinators, have been linked to emergent infection with the microsporidian Nosema bombi. Previous work has shown that phytochemicals in pollen and nectar can negatively impact parasites in individual bumblebees, but how this relates to social epidemiology and by extension whether plants can be effectively used as disease management strategies remains unexplored. Here we show that caffeine, identified in the nectar of Sainfoin, a constituent of agri-environment schemes, significantly reduced N. bombi infection intensity in individual bumblebees and, for the first time, that such effects impact social epidemiology, with colonies reared from wild caught queens having lower prevalence and intensity of infection. Furthermore, infection prevalence was lower in foraging bumblebees from these colonies, suggesting a likely reduction in population-level transmission. Our results demonstrate that phytochemicals can impact pollinator disease epidemiology and that planting strategies, which increase floral abundance to support biodiversity could be co-opted as disease management strategies.

ecology↗

bioRxiv2020

Pollen sterols are associated with phylogenetics and environment but not with pollinators

O_LIPhytosterols are primary plant metabolites that have fundamental structural and regulatory functions. They are also essential nutrients for phytophagous insects, including pollinators, that cannot synthesize sterols. Despite the well-described composition and diversity in vegetative plant tissues, few studies have examined phytosterol diversity in pollen. C_LIO_LIWe quantified 25 pollen phytosterols in 122 plant species (105 genera, 51 families) to determine their composition and diversity across plant taxa. We searched literature and databases for plant phylogeny, environmental conditions, and pollinator guilds of the species to examine the relationships with pollen sterols. C_LIO_LI24-methylenecholesterol, sitosterol and isofucosterol were the most common and abundant pollen sterols. We found phylogenetic clustering of twelve individual sterols, total sterol content and sterol diversity, and of sterol groupings that reflect their underlying biosynthesis pathway (24 carbon alkylation, ring B desaturation). Plants originating in tropical-like climates (higher mean annual temperature, lower temperature seasonality, higher precipitation in wettest quarter) were more likely to record higher pollen sterol content. However, pollen sterol composition and content showed no clear relationship with pollinator guilds. C_LIO_LIOur study is the first to show that pollen sterol diversity is phylogenetically clustered and that pollen sterol content may adapt to environmental conditions. C_LI

biochemistry↗

bioRxiv2020

Sweet and magnetic: Succession and CAZyme expression of marine bacterial communities encountering a mix of alginate and pectin particles

Polysaccharide particles are an important nutrient source and microhabitat for marine bacteria. However, substrate-specific bacterial dynamics in a mixture of particle types with different polysaccharide composition, as likely occurring in natural habitats, are undescribed. Here, we studied the composition, functional diversity and gene expression of marine bacterial communities encountering a mix of alginate and pectin particles. Communities were collected above macroalgal forests near Helgoland Island - where polysaccharide-rich particles might regularly occur - and exposed to a mix of magnetic particles of each polysaccharide, allowing the targeted evaluation by particle type. Amplicon, metagenome and metatranscriptome sequencing revealed that particle-associated (PA) and free-living (FL) communities significantly differed in composition and metabolism, whereas dynamics on alginate and pectin particles were unexpectedly similar. Amplicon sequence variants (ASVs) from Tenacibaculum, Colwellia, Psychrobium and Psychromonas dominated the community on both particle types. Corresponding metagenome-assembled genomes (MAGs) expressed diverse alginate lyases, several co-localized in polysaccharide utilization loci. One low-abundance MAG related to Catenovulum showed pectin specialization through upregulated GH53 and GH105 genes. A single Glaciecola ASV dominated the FL fraction, likely persisting on particle-derived oligomers through different glycoside hydrolases. The bacterial preference for alginate, whereas pectin mainly served as colonization scaffold, illuminates substrate-driven microbial dynamics within mixed polysaccharide resources. Moreover, elevated ammonium metabolism signifies nitrogen availability as important factor on particles, whereas elevated methylcitrate and glyoxylate cycles illustrate nutrient-limited conditions in the surrounding water. These insights expand our understanding of bacterial microscale ecology, niche specialization and the biological carbon pump in macroalgae-rich habitats.

microbiology↗

bioRxiv2020

High-dimensional association detection in large scale genomic data

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

bioinformatics↗

bioRxiv2020

Detecting local changes in chromatin architecture with false discovery control

Hi-C experiments are a powerful means to describe the organization of chromatin interactions genome-wide. By using Hi-C data to identify differentially organized genomic regions, relationships between this organization, gene expression, and cell identity may be established. However, Hi-C data exhibit a unique and challenging spatial structure, as genomic loci can show strong correlations when they are nearby in 3D space within the nucleus or 1D space along the chromosome. Consequently, the development of methods that can accurately detect differences between Hi-C samples while controlling false discoveries has remained difficult. To meet this need, we introduce a spatial modeling approach based on sliding window statistics. Using polymer simulations, we illustrate the improved power and precision of our method to identify differentially interacting genomic regions. We further demonstrate our methods ability to reveal biologically meaningful changes in chromatin architecture through two data analyses concerning the loss of architectural and chromatin remodeling proteins.

genomics↗

bioRxiv2020

Enrichment and physiological characterization of a novel comammox Nitrospira indicates ammonium inhibition of complete nitrification

The recent discovery of bacteria within the genus Nitrospira capable of complete ammonia oxidation (comammox) demonstrated that the sequential oxidation of ammonia to nitrate via nitrite can also be performed within a single bacterial cell. Although comammox Nitrospira exhibit a wide distribution in natural and engineered ecosystems, information on their physiological properties is scarce due to the limited number of cultured representatives. Furthermore, most available genomic information is derived from metagenomic sequencing and high-quality genomes of Nitrospira in general are limited. In this study, we obtained a high (90%) enrichment of a novel comammox species, tentatively named "Candidatus Nitrospira kreftii", and performed a detailed genomic and physiological characterization. The complete genome of "Ca. N. kreftii" allowed reconstruction of its basic metabolic traits. Similar to Nitrospira inopinata, the enrichment culture exhibited a very high ammonia affinity (Km(app)_NH3 {approx} 0.036 {micro}M), but a higher nitrite affinity (Km(app)_NO2- {approx} 13.8 {micro}M), indicating an adaptation to highly oligotrophic environments. Counterintuitively for a nitrifying microorganism, we also observed an inhibition of ammonia oxidation at ammonium concentrations as low as 25 {micro}M. This substrate inhibition of "Ca. N. kreftii" indicate that differences in ammonium tolerance rather than affinity can be a niche determining factor for different comammox Nitrospira.

microbiology↗

bioRxiv2020

Metagenomic profiling of ammonia- and methane-oxidizing microorganisms in a Dutch drinking water treatment plant

Elevated concentrations of ammonium and methane in groundwater can cause severe problems during drinking water production. To avoid their accumulation, raw water in the Netherlands, and many other countries, is purified by sand filtration. These drinking water filtration systems select for microbial communities that mediate the biodegradation of organic and inorganic compounds. In this study, the active layers and wall biofilm of a Dutch drinking water treatment plant (DWTP) were sampled at different locations along the filtration units of the plant over three years. We used high-throughput sequencing in combination with differential coverage and sequence composition-based binning to recover 56 near-complete metagenome-assembled genomes (MAGs) with an estimated completion of [≥]70% and with [≤]10% redundancy. These MAGs were used to characterize the microbial communities involved in the conversion of ammonia and methane. The methanotrophic microbial communities colonizing the wall biofilm (WB) and the granular material of the primary rapid sand filter (P-RSF) were dominated by members of the Methylococcaceae and Methylophilaceae. The abundance of these bacteria drastically decreased in the secondary rapid sand filter (S-RSF) samples. In all samples, complete ammonia-oxidizing (comammox) Nitrospira were the most abundant nitrifying guild. Clade A comammox Nitrospira dominated the P-RSF, while clade B was most abundant in WB and S-RSF, where ammonium concentrations were much lower. In conclusion, the knowledge obtained in this study contributes to understanding the role of microorganisms in the removal of carbon and nitrogen compounds during drinking water production. We furthermore found that drinking water treatment plants represent valuable model systems to study microbial community function and interaction. HighlightsO_LIMicrobial distribution was mainly influenced by sampling location within the DWTP C_LIO_LIClade A comammox Nitrospira were the dominant nitrifiers in the primary sand filter C_LIO_LIClade B was most abundant in samples from wall biofilm and the secondary filter C_LIO_LIA novel Methylophilaceae-affiliated methanotroph dominated the primary sand filter C_LI

microbiology↗

bioRxiv2020

Exploring the upper pH limits of nitrite oxidation: diversity, ecophysiology, and adaptive traits of haloalkalitolerant Nitrospira

Nitrite-oxidizing bacteria of the genus Nitrospira are key players of the biogeochemical nitrogen cycle. However, little is known about their occurrence and survival strategies in extreme pH environments. Here, we report on the discovery of physiologically versatile, haloalkalitolerant Nitrospira that drive nitrite oxidation at exceptionally high pH. Nitrospira distribution, diversity, and ecophysiology were studied in hypo- and subsaline (1.3-12.8 g salt/l), highly alkaline (pH 8.9-10.3) lakes by amplicon sequencing, metagenomics, and cultivation-based approaches. Surprisingly, not only were Nitrospira populations detected, but they were also considerably diverse with presence of members of Nitrospira lineages I, II and IV. Furthermore, the ability of Nitrospira enrichment cultures to oxidize nitrite at neutral to highly alkaline pH of 10.5 was demonstrated. Metagenomic analysis of a newly enriched Nitrospira lineage IV species, "Candidatus Nitrospira alkalitolerans", revealed numerous adaptive features of this organism to its extreme environment. Among them were a sodium-dependent N-type ATPase and NADH:quinone oxidoreductase next to the proton-driven forms usually found in Nitrospira. Other functions aid in pH and cation homeostasis and osmotic stress defense. "Ca. Nitrospira alkalitolerans" also possesses group 2a and 3b [NiFe] hydrogenases, suggesting it can use hydrogen as alternative energy source. These results reveal how Nitrospira cope with strongly fluctuating pH and salinity conditions and expand our knowledge of nitrogen cycling in extreme habitats.

microbiology↗

bioRxiv2020

Converting networks to predictive logic models from perturbation signalling data with CellNOpt

MotivationThe molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large data-sets is increasing steadily as new experimental approaches are developed. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. Logic models scale better with network size than alternative kinetic models, while keeping the interpretation of the model simple, making them particularly suitable for large datasets. ResultsCellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones. These updates include (i) an Integer Linear Programming (ILP) formulation which guarantees efficient optimisation for Boolean models, (ii) a probabilistic logic implementation for semi-quantitative datasets and (iii) the integration of MaBoSS, a stochastic Boolean simulator. Furthermore, we introduce Dynamic-Feeder, a tool to identify missing links not present in the prior knowledge. We have also implemented systematic post-hoc analyses to highlight the key components and parameters of our models. Finally, we provide an R-Shiny tool to run CellNOpt interactively. AvailabilityR-package(s): https://github.com/saezlab/cellnopt Contactjulio.saez@bioquant.uni-heidelberg.de Supplementary informationSupplemental Text.

bioinformatics↗

bioRxiv2019

Learning the properties of adaptive regions with functional data analysis

Identifying regions of positive selection in genomic data remains a challenge in population genetics. Most current approaches rely on comparing values of summary statistics calculated in windows. We present an approach termed SURFDAWave, which translates measures of genetic diversity calculated in genomic windows to functional data. By transforming our discrete data points to be outputs of continuous functions defined over genomic space, we are able to learn the features of these functions that signify selection. This enables us to confidently identify complex modes of natural selection, including adaptive introgression. We are also able to predict important selection parameters that are responsible for shaping the inferred selection events. By applying our model to human population-genomic data, we recapitulate previously identified regions of selective sweeps, such as OCA2 in Europeans, and predict that its beneficial mutation reached a frequency of 0.02 before it swept 1,802 generations ago, a time when humans were relatively new to Europe. In addition, we identify BNC2 in Europeans as a target of adaptive introgression, and predict that it harbors a beneficial mutation that arose in an archaic human population that split from modern humans within the hypothesized modern human-Neanderthal divergence range.

genomics↗

bioRxiv2019

PINK1 Regulates Dopamine and Lipids at Mitochondria to Maintain Synapses and Neuronal Function

Mitochondrial dysfunction contributes to the pathogenesis of Parkinsons disease but it is not clear why inherent mitochondrial defects lead specifically to the death of dopaminergic neurons of the mid brain. PINK1 is mitochondrial kinase and PINK1 mutations cause early onset Parkinsons disease.\n\nWe found that in neuronal progenitors, PINK1 regulates mitochondrial morphology, mitochondrial contact to the endoplasmic reticulum (ER) and the phosphorylation of Miro1. A compensatory metabolic shift towards lipid synthesis provides mitochondria with the components needed for membrane renewal and oxidative phosphorylation, maintaining the mitochondrial network once mature.\n\nCholesterol is increased by loss of PINK1, promoting overall membrane rigidity. This alters the distribution of phosphorylated DAT at synapses and impairs dopamine uptake. PINK1 is required for the phosphorylation of tyrosine hydroxylase at Ser19, dopamine and calcium homeostasis and dopaminergic pacemaking.\n\nWe suggest a novel mechanism for PINK1 pathogenicity in Parkinsons disease in addition to but not exclusive of mitophagy. We also provide a basis for potential therapeutics by showing that low doses of the cholesterol depleting drug {beta}-cyclodextrin reverse PINK1-specific phenotypes.

neuroscience↗

bioRxiv2019

Human cerebrospinal fluid induce neuronal excitability changes in resected human neocortical and hippocampal brain slices

Human cerebrospinal fluid (hCSF) have proven advantageous over conventional medium when culturing both rodent and human brain tissue. Increased excitability and synchronicity, similar to the active state exclusively recorded in vivo, reported in rodent slice and cell-cultures with hCSF as recording medium, indicates properties of the hCSF not matched by the artificial cerebrospinal fluid (aCSF) commonly used for electrophysiological recording. To evaluate the possible importance of using hCSF as electrophysiological recording medium of human brain tissue, we compared the general excitability in ex vivo human brain tissue slice cultures during perfusion with hCSF and aCSF. For measuring the general activity from a majority of neurons within neocortical and hippocampal human ex vivo slices we used a microelectrode array (MEA) recording technique with 252 electrodes covering an area of 3.2 x 3.2 mm2 and a second CMOS-based MEA with 4225 electrodes on a 2 x 2 mm2 area for detailed mapping of action potential waveforms. We found that hCSF increase the number of active neurons and the firing rate of the neurons in the slices as well as increasing the numbers of bursts while leaving the duration of the bursts unchanged. Interestingly, not only an increase in the overall activity in the slices was observed, but a reconfiguration of the network functionality could be detected with specific activation and inactivation of subpopulations of neuronal ensembles. In conclusion, hCSF is an important component to consider for future human tissue studies, especially for experiments designed to mimic the in vivo situation.

neuroscience↗

bioRxiv2019

Maximum likelihood estimation of species trees from gene trees in the presence of ancestral population structure

Though large multilocus genomic datasets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI, that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTIs performance in the four-taxon setting, and demonstrate the application of TASTI on a six-species Afrotropical mosquito dataset. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.

evolutionary biology↗

bioRxiv2019

MaxQuant software for ion mobility enhanced shotgun proteomics

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

bioinformatics↗

bioRxiv2018

Online parallel accumulation - serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, while mass spectra are acquired in about 100 microseconds with time-of-fight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive due to its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 800,000 fragmentation spectra in standard 120 min LC runs are easily achievable, which can be used for near exhaustive precursor selection in complex mixtures or re-sequencing weak precursors. MaxQuant identified more than 6,400 proteins in single run HeLa analyses without matching to a library, and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,900 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

systems biology↗