| 1. |
- Cossarizza, A., et al.
(author)
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Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
- 2019
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In: European Journal of Immunology. - : Wiley. - 0014-2980 .- 1521-4141. ; 49:10, s. 1457-1973
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Journal article (peer-reviewed)abstract
- These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
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| 2. |
- Aftab, Obaid, 1984-, et al.
(author)
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Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter
- 2014
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In: Autophagy. - : Informa UK Limited. - 1554-8627 .- 1554-8635. ; 10:1, s. 57-69
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Journal article (peer-reviewed)abstract
- Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.
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| 3. |
- Aftab, Obaid, 1984-, et al.
(author)
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Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing
- 2014
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In: Apoptosis (London). - : Springer Science and Business Media LLC. - 1360-8185 .- 1573-675X. ; 19:9, s. 1411-1418
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Journal article (peer-reviewed)abstract
- Label free time-lapse microscopy has opened a new avenue to the study of time evolving events in living cells. When combined with automated image analysis it provides a powerful tool that enables automated large-scale spatiotemporal quantification at the cell population level. Very few attempts, however, have been reported regarding the design of image analysis algorithms dedicated to the detection of apoptotic cells in such time-lapse microscopy images. In particular, none of the reported attempts is based on sufficiently fast signal processing algorithms to enable large-scale detection of apoptosis within hours/days without access to high-end computers. Here we show that it is indeed possible to successfully detect chemically induced apoptosis by applying a two-dimensional linear matched filter tailored to the detection of objects with the typical features of an apoptotic cell in phase-contrast images. First a set of recorded computational detections of apoptosis was validated by comparison with apoptosis specific caspase activity readouts obtained via a fluorescence based assay. Then a large screen encompassing 2,866 drug like compounds was performed using the human colorectal carcinoma cell line HCT116. In addition to many well known inducers (positive controls) the screening resulted in the detection of two compounds here reported for the first time to induce apoptosis.
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| 4. |
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| 5. |
- Chantzi, Efthymia
(author)
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Algorithmic discovery, development and personalized selection of higher-order drug cocktails : A label-free live-cell imaging & secretomics approach
- 2020
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Doctoral thesis (other academic/artistic)abstract
- An upward trend in clinical pharmacology is the use of multiple drugs to combat complex and co-occurring diseases due to better efficacy, decreased toxicity and reduced risk of evolving resistance. Despite high late-stage attrition rates and the need for multi drug treatments, most drug discovery and development efforts are still mainly focused on new one-size-fits-all monotherapies. This is unfortunate given the complex, heterogeneous and often only partially understood pathophysiology of many diseases. In this context, polypharmacotherapies hold strong potential, especially when patient tailored. However, as of today, the personalized combination therapy area remains vastly unexplored. A major reason is lack of standardized and robust tools that allow systematic in vitro drug combination sensitivity testing of different disease models and patient derived cells.This thesis fills in this lack by introducing two methodological frameworks, namely COMBImageDL and COMBSecretomics, designed to enable systematic second- and higher-order drug combination studies within and beyond cancer pharmacology. They include advanced quality control procedures, non-parametric resampling statistics to quantify uncertainty and a data driven methodology to evaluate response patterns and discern higher- from lower- and single-drug effects. Both are based on a standardized and reproducible format that could be employed with any experimental platform that provides the required raw data. COMBImageDL searches exhaustively for drug cocktails that induce changes in cell viability and time evolving cell culture morphology by employing conventional endpoint synergy analyses jointly with quantitative label-free live-cell imaging. Deep neural network learning, MapReduce parallel processing and method-specific parameter tuning are key components of the design. The purely phenotypic functionality of COMBImageDL is extended by COMBSecretomics, which searches exhaustively for drug cocktails that can modify, or even reverse malfunctioning secretomic patterns. It processes complex datasets involving drug treated cells observed before and after being stimulated by relevant proteins. Finally, the highest single agent method is generalized for higher-order drug combination analysis and adjusted for secreted protein profiles.The frameworks were used in five pharmacological studies being industrial, academic and clinical collaborations in areas where novel and personalized multi drug regimens are highly needed; oncology (acute myeloid leukemia and glioblastoma multiforme) and osteoarthritis. These studies demonstrate intriguing drug combination findings and in general the great potential of tools like COMBImageDL and COMBSecretomics to accelerate the discovery and development of novel potent polypharmacotherapeutic candidates.
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| 6. |
- Chantzi, Efthymia, et al.
(author)
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Exhaustive in vitro evaluation of the 9-drug cocktail CUSP9 for treatment of glioblastoma using COMBImageDL
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In: Molecular Cancer Therapeutics. - 1535-7163 .- 1538-8514.
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Journal article (peer-reviewed)abstract
- The CUSP9 protocol (aprepitant, auranofin, captopril, celecoxib, disulfiram, itraconazole, minocycline, quetiapine, sertraline) is currently undergoing a clinical trial as add-on treatment to standard-of-care temozolomide for recurrent glioblastoma. Although the theoretical repurposing rationale of this 9-drug cocktail is well defined, there is no in vitro experimental data yet supporting its superiority over all its plausible subsets. Such an exhaustive in vitro evaluation may provide preliminary evidence of whether only a fraction of all 9 drugs is needed to achieve an equivalent or even higher effect. Such information could be further used to guide and optimize individualized glioblastoma therapy selection both in terms of efficacy and adverse effects.Here, we employed COMBImageDL, a deep learning improved version of our recently developed COMBImage2 framework, to design, perform and analyze an exhaustive in vitro experiment of the CUSP9 protocol. More specifically, all 511 plausible subsets were evaluated as add-on treatment to temozolomide on a drug resistant glioblastoma cell line (M059K), by combining endpoint cell viability analysis and quantitative live-cell imaging. The experiment was performed in quadruplicate (eight 384-well plates, > 100GB of image data). Fixed clinically achievable concentrations were used for all drugs.Our results suggest that only disulfiram from the CUSP9 cocktail is required, together with temozolomide, in order to induce major changes in cell viability, confluence and morphology. Only slightly increased effects were observed by a few unique higher-order subsets of the CUSP9 protocol, which also contained disulfiram. This finding indicates that for the particular glioblastoma cell line used, the whole CUSP9 protocol could in principle be replaced solely with disulfiram. Notably, it may be worth testing in vitro the few slightly more potent higher-order subsets on primary patient derived glioblastoma cells. This work demonstrates the feasibility and potential of performing exhaustive in vitro evaluation of higher-order drug cocktails prior to subsequent assessment for clinical use. Although the experimental in vitro disease models are not optimal, they can still pinpoint which among all plausible subsets should be further considered. From a personalized therapy selection perspective, in vitro sensitivity testing of primary patient derived tumor cells could thereby advance from the current practice based on single drugs and only cytotoxicity readouts to also include higher-order drug cocktails and quantitative live-cell imaging.
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| 7. |
- Edberg, Anna, et al.
(author)
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Assessing Relative Bioactivity of Chemical Substances Using Quantitative Molecular Network Topology Analysis
- 2012
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In: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:5, s. 1238-1249
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Journal article (peer-reviewed)abstract
- Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17 beta-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.
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| 8. |
- Hammerling, Ulf, et al.
(author)
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Identifying Food Consumption Patterns among Young Consumers by Unsupervised and Supervised Multivariate Data Analysis
- 2014
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In: European Journal of Nutrition & Food Safety. - 2347-5641. ; 4:4, s. 392-403
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Journal article (peer-reviewed)abstract
- Although computational multivariate data analysis (MDA) already has been employed in the dietary survey area, the results reported are based mainly on classical exploratory (descriptive) techniques. Therefore, data of a Swedish and a Danish dietary survey on young consumers (4 to 5 years of age) were subjected not only to modern exploratory MDA, but also modern predictive MDA that via supervised learning yielded predictive classification models. The exploratory part, also encompassing Swedish 8 or 11-year old Swedish consumers, included new innovative forms of hierarchical clustering and bi-clustering. This resulted in several interesting multi-dimensional dietary patterns (dietary prototypes), including striking difference between those of the age-matched Danish and Swedish children. The predictive MDA disclosed additional multi-dimensional food consumption relationships. For instance, the consumption patterns associated with each of several key foods like bread, milk, potato and sweetened beverages, were found to differ markedly between the Danish and Swedish consumers. In conclusion, the joint application of modern descriptive and predictive MDA to dietary surveys may enable new levels of diet quality evaluation and perhaps also prototype-based toxicology risk assessment.
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| 9. |
- Herman, Stephanie, et al.
(author)
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Mass spectrometry based metabolomics for in vitro systems pharmacology : pitfalls, challenges, and computational solutions.
- 2017
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In: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 13:7
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Journal article (peer-reviewed)abstract
- INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.
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| 10. |
- Kashif, Muhammad, et al.
(author)
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A Pragmatic Definition of Therapeutic Synergy Suitable for Clinically Relevant In Vitro Multicompound Analyses
- 2014
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In: Molecular Cancer Therapeutics. - 1535-7163 .- 1538-8514. ; 13:7, s. 1964-1976
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Journal article (peer-reviewed)abstract
- For decades, the standard procedure when screening for candidate anticancer drug combinations has been to search for synergy, defined as any positive deviation from trivial cases like when the drugs are regarded as diluted versions of each other (Loewe additivity), independent actions (Bliss independence), or no interaction terms in a response surface model (no interaction). Here, we show that this kind of conventional synergy analysis may be completely misleading when the goal is to detect if there is a promising in vitro therapeutic window. Motivated by this result, and the fact that a drug combination offering a promising therapeutic window seldom is interesting if one of its constituent drugs can provide the same window alone, the largely overlooked concept of therapeutic synergy (TS) is reintroduced. In vitro TS is said to occur when the largest therapeutic window obtained by the best drug combination cannot be achieved by any single drug within the concentration range studied. Using this definition of TS, we introduce a procedure that enables its use in modern massively parallel experiments supported by a statistical omnibus test for TS designed to avoid the multiple testing problem. Finally, we suggest how one may perform TS analysis, via computational predictions of the reference cell responses, when only the target cell responses are available. In conclusion, the conventional error-prone search for promising drug combinations may be improved by replacing conventional (toxicology-rooted) synergy analysis with an analysis focused on (clinically motivated) TS.
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