Thomas Wolf

Metallic magnetic calorimeters

Metallic magnetic calorimeters (MMC) are calorimetric particle detectors, typically operated at temperatures below 100 mK, that make use of a paramagnetic temperature sensor to transform the temperature rise upon the absorption of a particle in the detector into a measurable magnetic flux change in a dc‐SQUID. During the last years a growing number of groups has started to develop MMC for a wide variety of applications, ranging from alpha‐, beta‐ and gamma‐spectrometry over the spatially resolved detection of accelerated molecule fragments to arrays of high resolution x‐ray detectors. For x‐rays with energies up to 6 keV an energy resolution of 2.7 eV (FWHM) has been demonstrated and we expect that this can be pushed below 1 eV with the next generation of devices. We give an introduction to the physics of MMCs and summarize the presently used readout schemes as well as the typically observed noise contributions and their impact on the energy resolution. We discuss general design considerations, the micro‐fabrication of MMCs and the performance of micro‐fabricated devices. In this field large progress has been achieved in the last years and the thermodynamic properties of most materials approach bulk values allowing for optimal and predictable performance.

Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas

BackgroundHigh-grade soft tissue sarcomas are a heterogeneous, complex group of aggressive malignant tumors showing mesenchymal differentiation. Recently, soft tissue sarcomas have increasingly been classified on the basis of underlying genetic alterations; however, the role of aberrant DNA methylation in these tumors is not well understood and, consequently, the usefulness of methylation-based classification is unclear.ResultsWe used the Infinium HumanMethylation27 platform to profile DNA methylation in 80 primary, untreated high-grade soft tissue sarcomas, representing eight relevant subtypes, two non-neoplastic fat samples and 14 representative sarcoma cell lines. The primary samples were partitioned into seven stable clusters. A classification algorithm identified 216 CpG sites, mapping to 246 genes, showing different degrees of DNA methylation between these seven groups. The differences between the clusters were best represented by a set of eight CpG sites located in the genes SPEG, NNAT, FBLN2, PYROXD2, ZNF217, COL14A1, DMRT2 and CDKN2A. By integrating DNA methylation and mRNA expression data, we identified 27 genes showing negative and three genes showing positive correlation. Compared with non-neoplastic fat, NNAT showed DNA hypomethylation and inverse gene expression in myxoid liposarcomas, and DNA hypermethylation and inverse gene expression in dedifferentiated and pleomorphic liposarcomas. Recovery of NNAT in a hypermethylated myxoid liposarcoma cell line decreased cell migration and viability.ConclusionsOur analysis represents the first comprehensive integration of DNA methylation and transcriptional data in primary high-grade soft tissue sarcomas. We propose novel biomarkers and genes relevant for pathogenesis, including NNAT as a potential tumor suppressor in myxoid liposarcomas.

Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis.

Mutations in POLE and survival of colorectal cancer patients - link to disease stage and treatment

Recent molecular profiling studies reported a new class of ultramutated colorectal cancers (CRCs), which are caused by exonuclease domain mutations (EDMs) in DNA polymerase ϵ (POLE). Data on the clinical implications of these findings as to whether these mutations define a unique CRC entity with distinct clinical outcome are lacking. We performed Sanger sequencing of the POLE exonuclease domain in 431 well‐characterized patients with microsatellite stable (MSS) CRCs of a population‐based patient cohort. Mutation data were analyzed for associations with major epidemiological, clinical, genetic, and pathological parameters including overall survival (OS) and disease‐specific survival (DSS). In 373 of 431 MSS CRC, all exons of the exonuclease domain were analyzable. Fifty‐four mutations were identified in 46 of these samples (12.3%). Besides already reported EDMs, we detected many new mutations in exons 13 and 14 (corresponding to amino acids 410–491) as well as in exon 9 and exon 11 (corresponding to aa 268–303 and aa 341–369). However, we did not see any significant associations of EDMs with clinicopathological parameters, including sex, age, tumor location and tumor stage, CIMP, KRAS, and BRAF mutations. While with a median follow‐up time of 5.0 years, survival analysis of the whole cohort revealed nonsignificantly different adjusted hazard ratios (HRs) of 1.35 (95% CI: 0.82–2.25) and 1.44 (0.81–2.58) for OS and DSS indicating slightly impaired survival of patients with EDMs, subgroup analysis for patients with stage III/IV disease receiving chemotherapy revealed a statistically significantly increased adjusted HR (1.87; 95%CI: 1.02–3.44). In conclusion, POLE EDMs do not appear to define an entirely new clinically distinct disease entity in CRC but may have prognostic or predictive implications in CRC subgroups, whose significance remains to be investigated in future studies.

Monitoring CSF Proteome Alterations in Amyotrophic Lateral Sclerosis: Obstacles and Perspectives in Translating a Novel Marker Panel to the Clinic

Background Amyotrophic lateral sclerosis (ALS) is a fatal disorder of the motor neuron system with poor prognosis and marginal therapeutic options. Current clinical diagnostic criteria are based on electrophysiological examination and exclusion of other ALS-mimicking conditions. Neuroprotective treatments are, however, most promising in early disease stages. Identification of disease-specific CSF biomarkers and associated biochemical pathways is therefore most relevant to monitor disease progression, response to neuroprotective agents and to enable early inclusion of patients into clinical trials. Methods and Findings CSF from 35 patients with ALS diagnosed according to the revised El Escorial criteria and 23 age-matched controls was processed using paramagnetic bead chromatography for protein isolation and subsequently analyzed by MALDI-TOF mass spectrometry. CSF protein profiles were integrated into a Random Forest model constructed from 153 mass peaks. After reducing this peak set to the top 25%, a classifier was built which enabled prediction of ALS with high accuracy, sensitivity and specificity. Further analysis of the identified peptides resulted in a panel of five highly sensitive ALS biomarkers. Upregulation of secreted phosphoprotein 1 in ALS-CSF samples was confirmed by univariate analysis of ELISA and mass spectrometry data. Further quantitative validation of the five biomarkers was achieved in an 80-plex Multiple Reaction Monitoring mass spectrometry assay. Conclusions ALS classification based on the CSF biomarker panel proposed in this study could become a valuable predictive tool for early clinical risk stratification. Of the numerous CSF proteins identified, many have putative roles in ALS-related metabolic processes, particularly in chromogranin-mediated secretion signaling pathways. While a stand-alone clinical application of this classifier will only be possible after further validation and a multicenter trial, it could be readily used to complement current ALS diagnostics and might also provide new insights into the pathomechanisms of this disease in the future.

Proteomic bronchiolitis obliterans syndrome risk monitoring in lung transplant recipients.

Background. Obliterative bronchiolitis poses a primary obstacle for long-term survival of lung transplant recipients and manifests clinically as bronchiolitis obliterans syndrome (BOS). Establishing a molecular level screening method to detect BOS-related proteome changes before its diagnosis by forced expiratory volume surrogate marker criteria was the main objective of this study. Methods. Bronchoalveolar lavage was performed in 82 lung transplant recipients (48/34 with/without known BOS development) at different time points between 12 and 48 months after lung transplantation. A mass spectrometry-based method was devised to generate bronchoalveolar lavage fluid proteome profiles that were screened for BOS-specific alterations. Statistically significant marker peptides and proteins were identified and validated by in-gel digestion, tandem mass spectrometric sequencing, and quantitative immunoassays. Results. Among the panel of statistically significant markers were Clara cell protein, calgranulin A, human neutrophil peptides, and the antimicrobial agent histatin. To assess their clinical relevance, a highly sensitive and specific classifier model was developed. Positive BOS classification by monitoring of seven polypeptides correlated strongly with a significant decrease in BOS-free time. Thus, it was possible to detect high-risk patients early on in the pathogenetic process. Conclusions. Monitoring the bronchoalveolar lavage fluid levels of seven polypeptides detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry allows a reliable prediction of early BOS using a Random Forest decision tree-based classifier model. The high accuracy of this robust model and its synergistic potential in combination with established forced expiratory volume-based diagnostics could make it an effective tool to supplement the current diagnostic regime after multicentric validation.

Bronchoalveolar lavage fluid of lung cancer patients: Mapping the uncharted waters using proteomics technology

The search for proteome-level markers of non-small cell lung cancer (NSCLC) has been mainly limited to serum or cell line screening approaches up to this point. We would like to demonstrate by this proof-of-principle study investigating bronchoalveolar lavage fluid samples from a cohort of NSCLC and control patients, that this readily available biofluid might be a more suitable source for discovering clinically usable NSCLC biomarkers.

Semiconductor-based sequencing of formalin-fixed, paraffin-embedded colorectal cancer samples.

We read with great interest the study by Zhang et al. [1], who reported on their experience with semiconductor-based sequencing of archival formalin-fixed, paraffin-embedded (FFPE) colorectal cancer (CRC) samples. By comparing the performance of variant detection using both the targeted polymerase chain reaction-based enrichment method from Ion Torrent (AmpliSeq cancer panel, version 1 [CHPv1]; Life Technologies, Inc., Grand Island, NY, https://www.lifetechnologies.com) and their own approach (SimpliSeq) for validation purposes, they recurrently detected false-positive mutations (variant frequency >5%) with CHPv1 in four genes (PIK3CA, NRAS, FGFR2, JAK2) that are well known to be important drivers and potentially druggable targets in several malignancies [2–4]. Additionally, they found low-frequency variants ( 2,000 FFPE samples sequenced across approximately 300 major cancer types, including CRC, covering the whole range from small biopsies to surgical resection specimens, we confirm that variants with T artifacts introduced by formalin) and requires variant calling with rigorously quality-controlled thresholds. In this context, we would like to highlight that according to our experience, the NanoDrop device (NanoDrop, Wilmington, DE, http://www.nanodrop.com) used for quality control in the study by Zhang et al. [1] overestimates the total quantity of DNA, so the amount of DNA used for library preparation may be too low, and fixation artifacts may in turn be overrepresented. In addition, we could not reproduce the results on recurrent false-positive mutations in PIK3CA and NRAS when screening our databases for CRC samples sequenced with the Personal Genome Machine/CHPv2 (PGM; Life Technologies, Inc.) and validated by conventional Sanger sequencing (Fig. 1). The supplemental online tables of Zhang et al. [1] reveal that all but one case with false-positive mutations in PIK3CA harbor the E545D variant in exon 10 (former nomenclature: exon 9). This is exceptional because E545D is exceedingly rare, according to COSMIC [9]. In line with these data and in contrast to Zhang et al. [1], we detected E545 variants of PIK3CA in 60 cases across different cancer types (28 CRC cases), but we have never yet observed E545D in any specimen, including CRC samples. Figure 1. Representative results for colorectal cancer cases harboring either PIK3CA E545 variants or NRAS Q61R mutations. Data were obtained by semiconductor-based sequencing using the AmpliSeq cancer panel (CHPv2) and validated by conventional Sanger sequencing. ... As stated by Zhang et al. [1], the detection of false-positive V617F variants of JAK2 has already been addressed and does not happen with the current CHPv2. False-positive mutations in NRAS were again confined to one specific variant, Q61R, in three different cases, with one case harboring a 5% variant. We detected and validated the Q61R variant in 19 CRC cases, indicating that the differences between CHPv1 and CHPv2 most likely account for the differences. Over the last 2 years, several updates of the Ion Torrent system have been released (chemistry, amplicon design, data analysis). In our experience, these modifications have improved sequencing accuracy with the AmpliSeq assay. In any case, although we consider the observations reported by Zhang et al. [1] to be important, it appears that the global statement of recurrent false positive mutations in cancer genes detected by Ion Torrent/AmpliSeq should be taken with some caution.

Krüppel-like zinc finger proteins in end-stage COPD lungs with and without severe alpha1-antitrypsin deficiency

BackgroundChronic obstructive pulmonary disease (COPD) is influenced by environmental and genetic factors. An important fraction of COPD cases harbor a major genetic determinant, inherited ZZ (Glu342Lys) α1-antitrypsin deficiency (AATD). A study was undertaken to investigate gene expression patterns in end-stage COPD lungs from patients with and without AATD.MethodsExplanted lungs of end-stage ZZ AATD-related (treated and non-treated with AAT augmentation therapy) and “normal” MM COPD, and liver biopsies from patients suffering from liver cirrhosis with and without ZZ AATD were used for gene expression analysis by Affymetrix microarrays or RT-PCR.ResultsA total of 162 genes were found to be differentially expressed (p-value ≤ 0.05 and |FC| ≥ 2) between MM and ZZ COPD patients. Of those, 134 gene sets were up-regulated and 28 were down-regulated in ZZ relative to MM lung tissue. A subgroup of genes, zinc finger protein 165, snail homolog 1 (Drosophila) (SNAI1), and Krüppel-like transcription factors (KLFs) 4 (gut), 9 and 10, perfectly segregated ZZ and MM COPD patients. The higher expression of KLF 9 and KLF10 has been verified in the replication cohort with AATD-related end-stage lung emphysema and liver cirrhosis. Furthermore, higher expression of KLF9, SNAI1 and DEFA1 was found in ZZ COPD lungs without augmentation therapy relative to MM COPD or ZZ COPD with augmentation therapy.ConclusionsThese results reveal the involvement of transcriptional regulators of the zinc-finger family in COPD pathogenesis and provide deeper insight into the pathophysiological mechanisms of COPD with and without AATD.

Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.

In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention.