Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work ...describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%.
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet ...suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
Abstract
Chronic viral infections are often associated with impaired CD8
+
T cell function, referred to as exhaustion. Although the molecular and cellular circuits involved in CD8
+
T cell exhaustion ...are well defined, with sustained presence of antigen being one important parameter, how much T cell receptor (TCR) signaling is actually ongoing in vivo during established chronic infection is unclear. Here, we characterize the in vivo TCR signaling of virus-specific exhausted CD8
+
T cells in a mouse model, leveraging TCR signaling reporter mice in combination with transcriptomics. In vivo signaling in exhausted cells is low, in contrast to their in vitro signaling potential, and despite antigen being abundantly present. Both checkpoint blockade and adoptive transfer of naïve target cells increase TCR signaling, demonstrating that engagement of co-inhibitory receptors curtails CD8
+
T cell signaling and function in vivo.
Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. ...Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5′-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.
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•Charting the metabolome response of E. coli to antibiotic treatment•Functional role of the rapid metabolic response in coping with antibiotic stress•The role for ammonium imbalance in aggravating chloramphenicol toxicity•dTDP-rhamnose regulates GyrA transcription in response to quinolone antibiotics
Zampieri et al. monitor short-term metabolic changes in Escherichia coli after exposure to antibiotics. A core set of metabolites exhibits a unique rapid response to antibiotic with common or radically different modes of action. By interfering with such cellular response, the authors reveal the functional role of metabolism in mediating the first immediate response to antibiotics.
Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural ...proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to
,
,
, and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species.
For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of ...processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R2 of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its ...flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of ...systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities.
Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling has proved to be a formal machine learning paradigm to achieve such interpretation by learning non-measurable hidden variables from observations. This review summarizes concepts and applications of this paradigm in the life sciences.
Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling has proved to be a formal machine learning paradigm to achieve such interpretation by learning non-measurable hidden variables from observations. This review summarizes concepts and applications of this paradigm in the life sciences.
The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit ...goals of systems biology that require the knowledge of the absolute quantity of the system's components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ∼10 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution.
The majority of all proteins expressed in the human osteosarcoma cell line U2OS were absolutely quantified by mass spectrometry. The quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell.
Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only ...partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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