Quantitative Biology Center

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

The human gut harbors a large and diverse community of commensal bacteria. Among them, Bifidobacterium is known to exhibit various probiotic effects including protection of hosts from infectious diseases. We recently discovered that genes encoding an ATP-binding-cassette-type carbohydrate transporter present in certain bifidobacteria contribute to protecting gnotobiotic mice from death induced by enterohemorrhagic Escherichia coli O157:H7. We elucidated the molecular mechanism on lethal infection in mice associated with several bifidobacterial strains by a multi-omics approach combining genomics, transcriptomics and metabolomics. The combined data clearly show that acetate produced by protective bifidobacteria acts in vivo to promote defense functions of the host epithelial cells and thereby protects the host from lethal infection. As demonstrated here, our multi-omics approach provides a powerful strategy for evaluation of host-microbial interactions in the complex gut ecosystem.

The broadening utilisation of ancient DNA to address archaeological, palaeontological, and biological questions is resulting in a rising diversity in the size of laboratories and scale of analyses being performed. In the context of this heterogeneous landscape, we present an advanced, and entirely redesigned and extended version of the EAGER pipeline for the analysis of ancient genomic data. This Nextflow pipeline aims to address three main themes: accessibility and adaptability to different computing configurations, reproducibility to ensure robust analytical standards, and updating the pipeline to the latest routine ancient genomic practices. The new version of EAGER has been developed within the nf-core initiative to ensure high-quality software development and maintenance support; contributing to a long-term life-cycle for the pipeline. nf-core/eager will assist in ensuring that a wider range of ancient DNA analyses can be applied by a diverse range of research groups and fields.

Grapevine (Vitis vinifera L.) is a species well known for its adaptation to radiation. However, photomorphogenic factors related to UV-B responses have not been molecularly characterized. We cloned and studied the role of UV-B RECEPTOR (UVR1), ELONGATED HYPOCOTYL 5 (HY5), and HY5 HOMOLOGUE (HYH) from V. vinifera We performed gene functional characterizations, generated co-expression networks, and tested them in different environmental conditions. These genes complemented the Arabidopsis uvr8 and hy5 mutants in morphological and secondary metabolic responses to radiation. We combined microarray and RNA sequencing (RNA-seq) data with promoter inspections to identify HY5 and HYH putative target genes and their DNA binding preferences. Despite sharing a large set of common co-expressed genes, we found different hierarchies for HY5 and HYH depending on the organ and stress condition, reflecting both co-operative and partially redundant roles. New candidate UV-B gene markers were supported by the presence of HY5-binding sites. These included a set of flavonol-related genes that were up-regulated in a HY5 transient expression assay. We irradiated in vitro plantlets and fruits from old potted vines with high and low UV-B exposures and followed the accumulation of flavonols and changes in gene expression in comparison with non-irradiated conditions. UVR1, HY5, and HYH expression varied with organ, developmental stage, and type of radiation. Surprisingly, UVR1 expression was modulated by shading and temperature in berries, but not by UV-B radiation. We propose that the UV-B response machinery favours berry flavonol accumulation through the activation of HY5 and HYH at different developmental stages at both high and low UV-B exposures.

Tuning of protein surface charge is a fundamental mechanism in biological systems. Protein charge is regulated in a physiological context by pH and interaction with counterions. We report on charge inversion and the related reentrant condensation in solutions of globular proteins with different multivalent metal cations. In particular, we focus on the changes in phase behavior and charge regulation due to pH effects caused by hydrolysis of metal ions. For several proteins and metal salts, charge inversion as measured by electrophoretic light scattering is found to be a universal phenomenon, the extent of which is dependent on the specific protein-salt combination. Reentrant phase diagrams show a much narrower phase-separated regime for acidic salts such as AlCl3 and FeCl3 compared to neutral salts such as YCl3 or LaCl3. The differences between acidic and neutral salts can be explained by the interplay of pH effects and binding of the multivalent counterions. The experimental findings are reproduced with good agreement by an analytical model for protein charging taking into account ion condensation, metal ion hydrolysis, and interaction with charged amino acid side chains on the protein surface. Finally, the relationship of charge inversion and reentrant condensation is discussed, suggesting that pH variation in combination with multivalent cations provides control over both attractive and repulsive interactions between proteins.

Post-translational histone modifications play key roles in gene regulation, development, and differentiation, but their dynamics in living organisms remain almost completely unknown. To address this problem, we developed a genetically encoded system for tracking histone modifications by generating fluorescent modification-specific intracellular antibodies (mintbodies) that can be expressed in vivo. To demonstrate, an H3 lysine 9 acetylation specific mintbody (H3K9ac-mintbody) was engineered and stably expressed in human cells. In good agreement with the localization of its target acetylation, H3K9ac-mintbody was enriched in euchromatin, and its kinetics measurably changed upon treatment with a histone deacetylase inhibitor. We also generated transgenic fruit fly and zebrafish stably expressing H3K9ac-mintbody for in vivo tracking. Dramatic changes in H3K9ac-mintbody localization during Drosophila embryogenesis could highlight enhanced acetylation at the start of zygotic transcription around mitotic cycle 7. Together, this work demonstrates the broad potential of mintbody and lays the foundation for epigenetic analysis in vivo.

Identification of lipids in nontargeted lipidomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) is still a major issue. While both accurate mass and fragment spectra contain valuable information, retention time (tR) information can be used to augment this data. We present a retention time model based on machine learning approaches which enables an improved assignment of lipid structures and automated annotation of lipidomics data. In contrast to common approaches we used a complex mixture of 201 lipids originating from fat tissue instead of a standard mixture to train a support vector regression (SVR) model including molecular structural features. The cross-validated model achieves a correlation coefficient between predicted and experimental test sample retention times of r = 0.989. Combining our retention time model with identification via accurate mass search (AMS) of lipids against the comprehensive LIPID MAPS database, retention time filtering can significantly reduce the rate of false positives in complex data sets like adipose tissue extracts. In our case, filtering with retention time information removed more than half of the potential identifications, while retaining 95% of the correct identifications. Combination of high-precision retention time prediction and accurate mass can thus significantly narrow down the number of hypotheses to be assessed for lipid identification in complex lipid pattern like tissue profiles.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. OBJECTIVES: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. GOVERNANCE AND POLICIES: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. USE CASES: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios. DISCUSSION: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets.We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies.We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. The identification and quantification of compounds in biological samples play a crucial role in biological and biomedical research, as concentration changes of specific metabolites may be descriptive of a system's response to disease or environmental influences. Liquid chromatography coupled to mass spectrometry (LC-MS) has become the primary analytic platform for metabolic profiling experiments (1Benton H.P. Wong D.M. Trauger S.A. Siuzdak G. XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization.Anal. Chem. 2008; 80: 6382-6389Crossref PubMed Scopus (209) Google Scholar). Such experiments usually follow one of two main strategies. Targeted metabolomics allows for the simultaneous, absolute quantification of hundreds of known metabolites by comparison to internal standards. In contrast, the aim of the nontargeted strategy is to detect as many metabolites in a biological system as possible. This raises two important issues: the identification of the metabolites in question, and their accurate quantification. Quantification based on the intensity ratio of isotope-labeled compound pairs has potential limitations, such as increased time and complexity of sample preparation, a requirement for increased sample concentrations, high costs of the reagents, and incomplete labeling. Thus, there has been increased interest in label-free techniques to achieve faster and simpler quantification results. In label-free quantitative metabolomics, control and case samples are analyzed via LC-MS individually, and quantification is achieved through the comparison of a metabolite's corresponding chromatographic peak intensities (2Zhu W. Smith J.W. Huang C.M. Mass spectrometry-based label-free quantitative proteomics.J. Biomed. Biotechnol. 2010; 2010: 840518Crossref PubMed Scopus (430) Google Scholar). An LC-MS profile from a complex biological sample usually yields several hundreds to tens of thousands of signals (3Yu T. Park Y. apLCMS—adaptive processing of high-resolution LC/MS data.Bioinformatics. 2009; 25: 1930-1936Crossref PubMed Scopus (238) Google Scholar). This complexity of the data often creates a bottleneck in data analysis for experimental studies (4Reinert K. Kohlbacher O. OpenMS and TOPP: open source software for LC-MS data analysis.Methods Mol. Biol. 2010; 604: 201-211Crossref PubMed Scopus (18) Google Scholar). Consequently, automated label-free quantification of metabolites from LC-MS data is an essential procedure for meeting today's experimental requirements. A typical data-processing pipeline includes centroiding, signal processing, feature detection, and retention time alignment (see supplemental Fig. S1). Starting from a set of profile data files, spectra are usually first centroided and then refined with signal processing methods such as noise/baseline reduction and mass recalibration. Feature detection is employed to extract ion signals from the raw or centroided data against the background of noise and to into a of peak intensities analysis and for LC-MS Chem. 2010; PubMed Scopus Google Scholar). methods samples to for and in the time and signals (4Reinert K. Kohlbacher O. OpenMS and TOPP: open source software for LC-MS data analysis.Methods Mol. Biol. 2010; 604: 201-211Crossref PubMed Scopus (18) Google Scholar). 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BACKGROUND & AIMS: We report a novel experimental immunotherapeutic approach in a patient with metastatic intrahepatic cholangiocarcinoma. In the 5year course of the disease, the initial tumor mass, two local recurrences and a lung metastasis were surgically removed. Lacking alternative treatment options, aiming at the induction of anti-tumor T cells responses, we initiated a personalized multi-peptide vaccination, based on in-depth analysis of tumor antigens (immunopeptidome) and sequencing. METHODS: Tumors were characterized by immunohistochemistry, next-generation sequencing and mass spectrometry of HLA ligands. RESULTS: Although several tumor-specific neo-epitopes were predicted in silico, none could be validated by mass spectrometry. Instead, a personalized multi-peptide vaccine containing non-mutated tumor-associated epitopes was designed and applied. Immunomonitoring showed vaccine-induced T cell responses to three out of seven peptides administered. The pulmonary metastasis resected after start of vaccination showed strong immune cell infiltration and perforin positivity, in contrast to the previous lesions. The patient remains clinically healthy, without any radiologically detectable tumors since March 2013 and the vaccination is continued. CONCLUSIONS: This remarkable clinical course encourages formal clinical studies on adjuvant personalized peptide vaccination in cholangiocarcinoma. LAY SUMMARY: Metastatic cholangiocarcinomas, cancers that originate from the liver bile ducts, have very limited treatment options and a fatal prognosis. We describe a novel therapeutic approach in such a patient using a personalized multi-peptide vaccine. This vaccine, developed based on the characterization of the patient's tumor, evoked detectable anti-tumor immune responses, associating with long-term tumor-free survival.

This paper describes the synthesis and optical properties of PbS/CdS quantum dots for <italic>in vivo</italic> fluorescence imaging.

In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape ( http://apps.cytoscape.org/apps/keggscape). KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function (Visual Style) and the ability to perform pathway analysis with a variety of Cytoscape apps. From the analyzed data, users can create complex and interactive visualizations which cannot be done in the KEGG PATHWAY web application. Experimental data with Affymetrix E. coli chips are used as an example to demonstrate how users can integrate pathways, annotations, and experimental data sets to create complex visualizations that clarify biological systems using KEGGscape and other Cytoscape apps.

We report a one-step synthetic strategy for the preparation of recombinant protein (EGFP-Protein G)-coated PbS quantum dots for dual (visible and second-NIR) fluorescence imaging of breast tumors at the cellular and whole-body level.

Immune checkpoint inhibitors (ICIs) belong to the therapeutic armamentarium in advanced hepatocellular carcinoma (HCC). However, only a minority of patients benefit from immunotherapy. Therefore, we aimed to identify indicators of therapy response. This multicenter analysis included 99 HCC patients. Progression-free (PFS) and overall survival (OS) were studied by Kaplan-Meier analyses for clinical parameters using weighted log-rank testing. Next-generation sequencing (NGS) was performed in a subset of 15 patients. The objective response (OR) rate was 19% median OS (mOS)16.7 months. Forty-one percent reached a PFS > 6 months; these patients had a significantly longer mOS (32.0 vs. 8.5 months). Child-Pugh (CP) A and B patients showed a mOS of 22.1 and 12.1 months, respectively. Ten of thirty CP-B patients reached PFS > 6 months, including 3 patients with an OR. Tumor mutational burden (TMB) could not predict responders. Of note, antibiotic treatment within 30 days around ICI initiation was associated with significantly shorter mOS (8.5 vs. 17.4 months). Taken together, this study shows favorable outcomes for OS with low AFP, OR, and PFS > 6 months. No specific genetic pattern, including TMB, could identify responders. Antibiotics around treatment initiation were associated with worse outcome, suggesting an influence of the host microbiome on therapy success.

This study investigated and established a method, using femtosecond laser processing, to fabricate a 100%-glass-based 12 μm ultra-thin and flexible micro-fluidic chip. First we investigated the suitable pulse energy of the laser to fabricate ultra-thin glass sheets and then we fabricated a prototype glass micro-fluidic chip. Two 1 mm-in-diameter orifices for facilitating alignment in the bonding step and one channel with a width of 20 μm and a length of 25 mm were fabricated in a 4 μm thickness ultra-thin glass sheet using the femtosecond laser; this forms layer 2 in the completed device. Next, the glass sheet with the channel was sandwiched between another glass sheet having an inlet hole and an outlet hole (layer 1) and a base glass sheet (layer 3); the three sheets were bonded to each other, resulting in a flexible, 100%-glass micro-fluidic chip with a thickness of approximately 12 μm and a weight of 3.6 mg. The basic function of the glass micro-fluidic chip was confirmed by flowing 1 and 2 μm in-diameter bead particles through the channel. The fabrication method clearly scales down the thickness limitation of flexible glass devices and offers a possible element technology for fabricating ultra-thin glass devices that can be applied to convection-enhanced delivery, implantable medical devices, wearable devices, and high-resolution imaging of small biological objects such as bacteria and proteins in the channel.

Acquired resistance to second generation BRAF inhibitors (BRAFis), like vemurafenib is limiting the benefits of long term targeted therapy for patients with malignant melanomas that harbor BRAF V600 mutations. Since many resistance mechanisms have been described, most of them causing a hyperactivation of the MAPK- or PI3K/AKT signaling pathways, one potential strategy to overcome BRAFi resistance in melanoma cells would be to target important common signaling nodes. Known factors that cause secondary resistance include the overexpression of receptor tyrosine kinases (RTKs), alternative splicing of BRAF or the occurrence of novel mutations in MEK1 or NRAS. In this study we show that β-catenin is stabilized and translocated to the nucleus in approximately half of the melanomas that were analyzed and which developed secondary resistance towards BRAFi. We further demonstrate that β-catenin is involved in the mediation of resistance towards vemurafenib in vitro and in vivo. Unexpectedly, β-catenin acts mainly independent of the TCF/LEF dependent canonical Wnt-signaling pathway in resistance development, which partly explains previous contradictory results about the role of β-catenin in melanoma progression and therapy resistance. We further demonstrate that β-catenin interacts with Stat3 after chronic vemurafenib treatment and both together cooperate in the acquisition and maintenance of resistance towards BRAFi.

. During the summer, shifts in runoff and soil moisture influence redox conditions and therefore the balance of Fe oxidation and reduction. Whether reactive iron minerals could act as a stable sink for carbon or whether they are continuously dissolved and reprecipitated during redox shifts remains unknown. We deployed bags of synthetic ferrihydrite (FH)-coated sand in the active layer along a permafrost thaw gradient in Stordalen mire (Abisko, Sweden) over the summer (June to September) to capture changes in redox conditions and quantify the formation and dissolution of reactive Fe(III) (oxyhydr)oxides. We found that the bags accumulated Fe(III) under constant oxic conditions in areas overlying intact permafrost over the full summer season. In contrast, in fully thawed areas, conditions were continuously anoxic, and by late summer, 50.4 ± 12.8% of the original Fe(III) (oxyhydr)oxides were lost via dissolution. Periodic redox shifts (from 0 to +300 mV) were observed over the summer season in the partially thawed areas. This resulted in the dissolution and loss of 47.2 ± 20.3% of initial Fe(III) (oxyhydr)oxides when conditions are wetter and more reduced, and new formation of Fe(III) minerals (33.7 ± 8.6% gain in comparison to initial Fe) in the late summer under more dry and oxic conditions, which also led to the sequestration of Fe-bound organic carbon. Our data suggest that there is seasonal turnover of iron minerals in partially thawed permafrost peatlands, but that a fraction of the Fe pool remains stable even under continuously anoxic conditions.

Genomic DNA is organized three dimensionally within cells as chromatin and is searched and read by various proteins by an unknown mechanism; this mediates diverse cell functions. Recently, several pieces of evidence, including our cryomicroscopy and synchrotron X-ray scattering analyses, have demonstrated that chromatin consists of irregularly folded nucleosome fibers without a 30-nm chromatin fiber (i.e., a polymer melt-like structure). This melt-like structure implies a less physically constrained and locally more dynamic state, which may be crucial for protein factors to scan genomic DNA. Using a combined approach of fluorescence correlation spectroscopy, Monte Carlo computer simulations, and single nucleosome imaging, we demonstrated the flexible and dynamic nature of the nucleosome fiber in living mammalian cells. We observed local nucleosome fluctuation (~50 nm movement/30 ms) caused by Brownian motion. Our in vivo/in silico results suggest that local nucleosome dynamics facilitate chromatin accessibility and play a critical role in the scanning of genome information.

By using in-cell NMR experiments, we have demonstrated that the protein folding state in cells is significantly influenced by the cellular health conditions. hAK1 was denatured in cells under stressful culture conditions, while it remained functional and properly folded in cells continuously supplied with a fresh medium.

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.