| 1. |
- Ambikan, Anoop T., et al.
(författare)
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Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity
- 2022
-
Ingår i: Cell systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 13:8, s. 665-681
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Tidskriftsartikel (refereegranskat)abstract
- The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolo-mics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a sub-stantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and person-alized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as a-ketoglutarate, succi-nate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.
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| 2. |
- Demichev, Vadim, et al.
(författare)
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A time-resolved proteomic and prognostic map of COVID-19
- 2021
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
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| 3. |
- Heinke, Paula, et al.
(författare)
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Diploid hepatocytes drive physiological liver renewal in adult humans
- 2022
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Ingår i: CELL SYSTEMS. - : Elsevier. - 2405-4712 .- 2405-4720. ; 13:6, s. 499-
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Tidskriftsartikel (refereegranskat)abstract
- Physiological liver cell replacement is central to maintaining the organ's high metabolic activity, although its characteristics are difficult to study in humans. Using retrospective radiocarbon (C-14) birth dating of cells, we report that human hepatocytes show continuous and lifelong turnover, allowing the liver to remain a young organ (average age <3 years). Hepatocyte renewal is highly dependent on the ploidy level. Diploid hepatocytes show more than 7-fold higher annual birth rates than polyploid hepatocytes. These observations support the view that physiological liver cell renewal in humans is mainly dependent on diploid hepatocytes, whereas polyploid cells are compromised in their ability to divide. Moreover, cellular transitions between diploid and polyploid hepatocytes are limited under homeostatic conditions. With these findings, we present an integrated model of homeostatic liver cell generation in humans that provides fundamental insights into liver cell turnover dynamics.
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| 4. |
- Lahtvee, Petri-Jaan, 1985, et al.
(författare)
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Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast
- 2017
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 4:5, s. 495-504.e5
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Tidskriftsartikel (refereegranskat)abstract
- Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.
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| 5. |
- Marabita, Francesco, et al.
(författare)
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Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health
- 2022
-
Ingår i: Cell Systems. - : Cell Press. - 2405-4712 .- 2405-4720. ; 13:3, s. 241-255.e7
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Tidskriftsartikel (refereegranskat)abstract
- We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.
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| 6. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Broad Views of Non-alcoholic Fatty Liver Disease
- 2018
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 6:1, s. 37-51.e9
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks. Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.
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| 7. |
- Messner, Christoph B., et al.
(författare)
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Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
- 2020
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 11:1, s. 11-24.E4
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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| 8. |
- Nilsson, Avlant, 1985, et al.
(författare)
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Metabolic Models of Protein Allocation Call for the Kinetome
- 2017
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 5:6, s. 538-541
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way.
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| 9. |
- Piening, B. D., et al.
(författare)
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Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
- 2018
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 6:2
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Tidskriftsartikel (refereegranskat)abstract
- Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.
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| 10. |
- Zelezniak, Aleksej, 1984, et al.
(författare)
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Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts
- 2018
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 7:3, s. 269-283
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Tidskriftsartikel (refereegranskat)abstract
- A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation.
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| 11. |
- Antoniou-Kourounioti, Rea L., et al.
(författare)
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Temperature Sensing Is Distributed throughout the Regulatory Network that Controls FLC Epigenetic Silencing in Vernalization
- 2018
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Ingår i: Cell systems. - : Elsevier BV. - 2405-4712. ; 7:6, s. 643-655
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Tidskriftsartikel (refereegranskat)abstract
- Many organisms need to respond to complex, noisy environmental signals for developmental decision making. Here, we dissect how Arabidopsis plants integrate widely fluctuating field temperatures over month-long timescales to progressively upregulate VERNALIZATION INSENSITIVE3 (VIN3) and silence FLOWERING LOCUS C (FLC), aligning flowering with spring. We develop a mathematical model for vernalization that operates on multiple timescales-long term (month), short term (day), and current (hour)-and is constrained by experimental data. Our analysis demonstrates that temperature sensing is not localized to specific nodes within the FLC network. Instead, temperature sensing is broadly distributed, with each thermosensory process responding to specific features of the plants' history of exposure to warm and cold. The model accurately predicts FLC silencing in new field data, allowing us to forecast FLC expression in changing climates. We suggest that distributed thermosensing may be a general property of thermoresponsive regulatory networks in complex natural environments.
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| 12. |
- Baliga, NS, et al.
(författare)
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The State of Systems Genetics in 2017
- 2017
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Ingår i: Cell systems. - : Elsevier BV. - 2405-4712. ; 4:1, s. 7-15
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 13. |
- Butler, L. M., et al.
(författare)
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Analysis of Body-wide Unfractionated Tissue Data to Identify a Core Human Endothelial Transcriptome
- 2016
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Ingår i: Cell Systems. - : Cell Press. - 2405-4712. ; 3:3, s. 287-301.e3
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Tidskriftsartikel (refereegranskat)abstract
- Endothelial cells line blood vessels and regulate hemostasis, inflammation, and blood pressure. Proteins critical for these specialized functions tend to be predominantly expressed in endothelial cells across vascular beds. Here, we present a systems approach to identify a panel of human endothelial-enriched genes using global, body-wide transcriptomics data from 124 tissue samples from 32 organs. We identified known and unknown endothelial-enriched gene transcripts and used antibody-based profiling to confirm expression across vascular beds. The majority of identified transcripts could be detected in cultured endothelial cells from various vascular beds, and we observed maintenance of relative expression in early passage cells. In summary, we describe a widely applicable method to determine cell-type-specific transcriptome profiles in a whole-organism context, based on differential abundance across tissues. We identify potential vascular drug targets or endothelial biomarkers and highlight candidates for functional studies to increase understanding of the endothelium in health and disease.
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| 14. |
- Cao, Junyue, et al.
(författare)
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Principles of Systems Biology, No. 21
- 2017
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 5:3, s. 158-160
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This month: relating single cells to populations (Cao/Packer, Wu/Altschuler, O'Brien, Friedman), an excess of ribosomes (Barkai), human pathology atlas (Uhlen), signatures of feedback (Rahi), and major genome redesign (Baumgart).
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| 15. |
- Cao, Junyue, et al.
(författare)
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Principles of Systems Biology, No. 21 : Editorial
- 2017
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Ingår i: CELL SYSTEMS. - : Elsevier BV. - 2405-4712. ; 5:3, s. 158-160
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This month: relating single cells to populations (Cao/Packer, Wu/Altschuler, O'Brien, Friedman), an excess of ribosomes (Barkai), human pathology atlas (Uhlen), signatures of feedback (Rahi), and major genome redesign (Baumgart).
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| 16. |
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| 17. |
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| 18. |
- Elf, Johan
(författare)
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Hypothesis : Homologous Recombination Depends on Parallel Search
- 2016
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 3:4, s. 325-327
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Tidskriftsartikel (refereegranskat)abstract
- It is not known how a cell manages to find a specific DNA sequence sufficiently fast to repair a broken chromosome through homologous recombination. I propose a solution based on freely diffusing molecules that are programmed with sequences corresponding to those flanking the break site. In such a process, the target search would be parallelized.
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| 19. |
- Elf, Johan
(författare)
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Staying Clear of the Dragons
- 2016
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Ingår i: CELL SYSTEMS. - : Elsevier BV. - 2405-4712. ; 2:4, s. 219-220
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Tidskriftsartikel (refereegranskat)
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| 20. |
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| 21. |
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| 22. |
- Heusel, Moritz, et al.
(författare)
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A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS
- 2020
-
Ingår i: Cell systems. - : Elsevier BV. - 2405-4712. ; 10:2, s. 6-155
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Tidskriftsartikel (refereegranskat)abstract
- Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.
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| 23. |
- Hollandi, R., et al.
(författare)
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nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
- 2020
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712. ; 10:5, s. 453-458.e6
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Tidskriftsartikel (refereegranskat)abstract
- Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. Microscopy image analysis of single cells can be challenging but also eased and improved. We developed a deep learning method to segment cell nuclei. Our strategy is adapting to unexpected circumstances automatically by synthesizing artificial microscopy images in such a domain as training samples.
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| 24. |
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| 25. |
- Kutsche, Lisa K., et al.
(författare)
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Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis
- 2018
-
Ingår i: Cell systems. - : Elsevier BV. - 2405-4712. ; 7:4, s. 438-452
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Tidskriftsartikel (refereegranskat)abstract
- Non-coding RNAs regulate many biological processes including neurogenesis. The brain-enriched miR-124 has been assigned as a key player of neuronal differentiation via its complex but little understood regulation of thousands of annotated targets. To systematically chart its regulatory functions, we used CRISPR/Cas9 gene editing to disrupt all six miR-124 alleles in human induced pluripotent stem cells. Upon neuronal induction, miR-124-deleted cells underwent neurogenesis and became functional neurons, albeit with altered morphology and neurotransmitter specification. Using RNA-induced-silencing-complex precipitation, we identified 98 high-confidence miR-124 targets, of which some directly led to decreased viability. By performing advanced transcription-factor-network analysis, we identified indirect miR-124 effects on apoptosis, neuronal subtype differentiation, and the regulation of previously uncharacterized zinc finger transcription factors. Our data emphasize the need for combined experimental- and system-level analyses to comprehensively disentangle and reveal miRNA functions, including their involvement in the neurogenesis of diverse neuronal cell types found in the human brain.
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| 26. |
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| 27. |
- Madrigal, Pedro, et al.
(författare)
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Revamping Space-omics in Europe
- 2020
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Ingår i: CELL SYSTEMS. - : Elsevier BV. - 2405-4712. ; 11:6, s. 555-556
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Tidskriftsartikel (refereegranskat)
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| 28. |
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| 29. |
- O'Hagan, Steve, et al.
(författare)
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GeneGini : Assessment via the Gini Coefficient of Reference "Housekeeping'' Genes and Diverse Human Transporter Expression Profiles
- 2018
-
Ingår i: Cell Systems. - : Cell Press. - 2405-4712. ; 6:2, s. 230-
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Tidskriftsartikel (refereegranskat)abstract
- The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping'' genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.
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| 30. |
- Pagella, Pierfrancesco, et al.
(författare)
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The time-resolved genomic impact of Wnt/(3-catenin signaling
- 2023
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 14:7, s. 563-581.e7
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Tidskriftsartikel (refereegranskat)abstract
- Wnt signaling orchestrates gene expression via its effector, (3-catenin. However, it is unknown whether (3-cat-enin binds its target genomic regions simultaneously and how this impacts chromatin dynamics to modulate cell behavior. Using a combination of time-resolved CUT & RUN against (3-catenin, ATAC-seq, and perturba-tion assays in different cell types, we show that Wnt/(3-catenin physical targets are tissue-specific, (3-catenin "moves"on different loci over time, and its association to DNA accompanies changing chromatin accessi-bility landscapes that determine cell behavior. In particular, Wnt/(3-catenin progressively shapes the chro-matin of human embryonic stem cells (hESCs) as they undergo mesodermal differentiation, a behavior that we define as "plastic."In HEK293T cells, on the other hand, Wnt/(3-catenin drives a transient chromatin open-ing, followed by re-establishment of the pre-stimulation state, a response that we define as "elastic."Future experiments shall assess whether other cell communication mechanisms, in addition to Wnt signaling, are ruled by time, cellular idiosyncrasies, and chromatin constraints. A record of this papers transparent peer review process is included in the supplemental information.
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| 31. |
- Piccinini, Filippo, et al.
(författare)
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Advanced Cell Classifier : User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data
- 2017
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 4:6, s. 651-
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Tidskriftsartikel (refereegranskat)abstract
- High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.
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| 32. |
- Pozniak, Yair, et al.
(författare)
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System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis
- 2016
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Ingår i: CELL SYSTEMS. - : Elsevier BV. - 2405-4712. ; 2:3, s. 172-184
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Tidskriftsartikel (refereegranskat)abstract
- The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.
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| 33. |
- Salamon, John, et al.
(författare)
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Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet
- 2018
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712. ; 6:5, s. 626-630
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Tidskriftsartikel (refereegranskat)abstract
- In situ sequencing methods generate spatially resolved RNA localization and expression data at an almost single-cell resolution. Few methods, however, currently exist to analyze and visualize the complex data that is produced, which can encode the localization and expression of a million or more individual transcripts in a tissue section. Here, we present InsituNet, an application that converts in situ sequencing data into interactive network-based visualizations, where each unique transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet is available as an app for the Cytoscape platform at http://apps.cytoscape.org/apps/insitunet. InsituNet enables the analysis of the relationships that exist between these transcripts and can uncover how spatial co-expression profiles change in different regions of the tissue or across different tissue sections.
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| 34. |
- Smith, Kevin, 1975-, et al.
(författare)
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Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
- 2018
-
Ingår i: CELL SYSTEMS. - : Elsevier. - 2405-4712. ; 6:6, s. 636-653
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Forskningsöversikt (refereegranskat)abstract
- Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.
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| 35. |
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| 36. |
- Wayne, Greg, et al.
(författare)
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Principles of Systems Biology, No. 11
- 2016
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 3:5, s. 406-410
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Tidskriftsartikel (refereegranskat)abstract
- This month: AI that learns patterns and facts, new protein-RNA and protein-protein relationships, engineering signaling and metabolism, and more variants of Cas9.
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