Daniel Ruderman

A Cross-platform Toolkit for Mass Spectrometry and Proteomics

Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

Naphthenic acids as indicators of crude oil biodegradation in soil, based on semi-quantitative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry.

Crude oil contaminated soil cores were collected from a basin that contained oily solids left from three decades of oil production. Hydrocarbon biomarker analyses revealed that the soil extracts were moderately biodegraded compared with the non-degraded source oil. The degree of biodegradation also decreased with core depth (7 cm to 1 m). These data were correlated to compositional changes observed in acidic NSO-compounds that were selectively ionized and mass resolved by negative ion electrospray Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). Among the NSO-compounds ionized, the increase in naphthenic acid concentration (e.g., acyclic and alicyclic carboxylic acids) best correlated with the increase in biodegradation (e.g., from non-degraded to moderately degraded) as determined by the hydrocarbon biomarker analyses. The most biodegraded surface extracts (7 cm) exhibited an 80% increase in the abundance of acids relative to the source oil. Use of an internal standard allowed the semi-quantitative determination of the total naphthenic acid concentration, which decreased significantly (P < 0.05) with soil depth. Furthermore, the shift to higher double bond equivalents (DBEs), from acyclic to alicyclic acids, indicated that the increase in acids in the soil extracts was predominantly due to biotic processes. This work demonstrates the potential of ESI FT-ICR MS as a semi-quantitative tool to monitor the production of naphthenic acids during crude oil biotransformation in the environment.

Anterior gradient 2 (AGR2): Blood-based biomarker elevated in metastatic prostate cancer associated with the neuroendocrine phenotype

Anterior gradient 2 (AGR2) is associated with metastatic progression in prostate cancer cells as well as other normal and malignant tissues. We investigated AGR2 expression in patients with metastatic prostate cancer.

Designing Successful Proteomics Experiments

Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.

Single cell dynamic phenotyping

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

The emergence of dynamic phenotyping

Recent experiments have revealed the importance of discerning temporal response behaviors in single cells. Such Bdynamic phenotypes^ cannot be seen when averagingmeasurements across many cells because different behaviors blur together. Many such results focus on changes in protein abundance, intracellular localization, and cell shape in response to stimuli, processes that require tracking living cells for minutes to hours. These discoveries have been enabled by live cell microscopy and analysis techniques that have become available only recently. In a recent Editorial (Wang et al. 2017), Wang et al. described many methods for assessing single-cell phenotypes. Here, I focus on how the cross-disciplinary nature of these techniques will be central to their advancement. The best characterized example of a dynamic phenotype is the abundance changes of p53 in response to DNA damage. Based on Western blot studies, it was known that strong DNA damage causes damped multihour p53 oscillations, whose amplitude increases with damage (Bar-or et al. 2000). By looking at the dynamics of individual cells instead of populations using live cell imaging, Lahav et al. (2004) found something unexpected: single cells themselves do not have damped oscillations. Instead, cells have undamped oscillations with varying numbers of cycles. AWestern blot simply averages these varied responses into a damped oscillation. The increased response amplitude with DNA damage is also a population effect: higher DNA damage pushes more cells to longer oscillations, which add up in phase to a larger population amplitude. Thus taking the population average behavior as true for single cells can be misleading. Dynamic phenotypes also relate to disease and therapeutic response. In HCT-116 cells, those with fast nuclear p53 accumulation after cisplatin exposure undergo apoptosis, whereas those with slow accumulation survive (Paek et al. 2016). In single lung cancer cells, early intracellular spatio-temporal dynamics correlate with TNF-α sensitivity (Loo et al. 2017). In type 2 diabetes, pulsatile release of insulin by beta islet cells is impaired (O’Rahilly et al. 1988). Yang et al. (2017) recently showed that altering p53 dynamics changes cell fate, demonstrating a causal link to response phenotype. Thus it may be valuable to characterize such disease states by how dynamical processes deviate from normal, which we might call Bdynopathy.^ Such aberrant dynamics and signaling networks are in fact disease targets in their own right: Stewart-Ornstein and Lahav (2017), recently employed phenotypic profiling to discover small molecules that modulate p53 dynamics. Of the many technical advances that have led to these discoveries, which are most important? High content screening of live cells, the delivery of time varying stimulation, and advanced microscopy image analysis platforms for tracking cells have all been critical. See (Gaudet and Miller-Jensen 2016; Handly et al. 2016; Cell Biol Toxicol DOI 10.1007/s10565-017-9413-x

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.

Epigenetic changes mediated by polycomb repressive complex 2 and E2a are associated with drug resistance in a mouse model of lymphoma

BackgroundThe genetic origins of chemotherapy resistance are well established; however, the role of epigenetics in drug resistance is less well understood. To investigate mechanisms of drug resistance, we performed systematic genetic, epigenetic, and transcriptomic analyses of an alkylating agent-sensitive murine lymphoma cell line and a series of resistant lines derived by drug dose escalation.MethodsDose escalation of the alkylating agent mafosfamide was used to create a series of increasingly drug-resistant mouse Burkitt’s lymphoma cell lines. Whole genome sequencing, DNA microarrays, reduced representation bisulfite sequencing, and chromatin immunoprecipitation sequencing were used to identify alterations in DNA sequence, mRNA expression, CpG methylation, and H3K27me3 occupancy, respectively, that were associated with increased resistance.ResultsOur data suggest that acquired resistance cannot be explained by genetic alterations. Based on integration of transcriptional profiles with transcription factor binding data, we hypothesize that resistance is driven by epigenetic plasticity. We observed that the resistant cells had H3K27me3 and DNA methylation profiles distinct from those of the parental lines. Moreover, we observed DNA methylation changes in the promoters of genes regulated by E2a and members of the polycomb repressor complex 2 (PRC2) and differentially expressed genes were enriched for targets of E2a. The integrative analysis considering H3K27me3 further supported a role for PRC2 in mediating resistance. By integrating our results with data from the Immunological Genome Project (Immgen.org), we showed that these transcriptional changes track the B-cell maturation axis.ConclusionsOur data suggest a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B-cell epigenome; these alterations attenuate alkylating agent treatment-induced apoptosis.

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated “public data libraries”, and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

Intracellular kinetics of the androgen receptor shown by multimodal Image Correlation Spectroscopy (mICS)

The androgen receptor (AR) pathway plays a central role in prostate cancer (PCa) growth and progression and is a validated therapeutic target. In response to ligand binding AR translocates to the nucleus, though the molecular mechanism is not well understood. We therefore developed multimodal Image Correlation Spectroscopy (mICS) to measure anisotropic molecular motion across a live cell. We applied mICS to AR translocation dynamics to reveal its multimodal motion. By integrating fluorescence imaging methods we observed evidence for diffusion, confined movement, and binding of AR within both the cytoplasm and nucleus of PCa cells. Our findings suggest that in presence of cytoplasmic diffusion, the probability of AR crossing the nuclear membrane is an important factor in determining the AR distribution between cytoplasm and the nucleus, independent of functional microtubule transport. These findings may have implications for the future design of novel therapeutics targeting the AR pathway in PCa.