Tumor Biomechanics Alters Metastatic Dissemination of Triple Negative Breast Cancer via Rewiring Fatty Acid Metabolism.

In recent decades, the role of tumor biomechanics on cancer cell behavior at the primary site has been increasingly appreciated. However, the effect of primary tumor biomechanics on the latter stages of the metastatic cascade, such as metastatic seeding of secondary sites and outgrowth remains underappreciated. This work sought to address this in the context of triple negative breast cancer (TNBC), a cancer type known to aggressively disseminate at all stages of disease progression. Using mechanically tuneable model systems, mimicking the range of stiffness's typically found within breast tumors, it is found that, contrary to expectations, cancer cells exposed to softer microenvironments are more able to colonize secondary tissues. It is shown that heightened cell survival is driven by enhanced metabolism of fatty acids within TNBC cells exposed to softer microenvironments. It is demonstrated that uncoupling cellular mechanosensing through integrin ß1 blocking antibody effectively causes stiff primed TNBC cells to behave like their soft counterparts, both in vitro and in vivo. This work is the first to show that softer tumor microenvironments may be contributing to changes in disease outcome by imprinting on TNBC cells a greater metabolic flexibility and conferring discrete cell survival advantages.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

In situ single-cell profiling sheds light on IFI27 localisation during SARS-CoV-2 infection.

The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.

Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification.

Flow cytometers are robust and ubiquitous tools of biomedical research, as they enable high-throughput fluorescence-based multi-parametric analysis and sorting of single cells. However, analysis is often constrained by the availability of detection reagents or functional changes of cells caused by fluorescent staining. Here, we introduce MAPS-FC (multi-angle pulse shape flow cytometry), an approach that measures angle- and time-resolved scattered light for high-throughput cell characterization to circumvent the constraints of conventional flow cytometry. In order to derive cell-specific properties from the acquired pulse shapes, we developed a data analysis procedure based on wavelet transform and k-means clustering. We analyzed cell cycle stages of Jurkat and HEK293 cells by MAPS-FC and were able to assign cells to the G1, S, and G2/M phases without the need for fluorescent labeling. The results were validated by DNA staining and by sorting and re-analysis of isolated G1, S, and G2/M populations. Our results demonstrate that MAPS-FC can be used to determine cell properties that are otherwise only accessible by invasive labeling. This approach is technically compatible with conventional flow cytometers and paves the way for label-free cell sorting.

K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods.

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised K-nearest neighbour vectors of a spatially random point pattern. Here, we use KNA to define a novel visualisation of individual images, to compare within and between groups of images and to investigate the preferential patterns of phosphorylation. This methodology is also highly flexible and can be used to augment existing clustering methods by providing clustering diagnostics as well as revealing substructure within microclusters. In summary, we have presented a highly flexible analysis tool that presents new conceptual possibilities in the analysis of SMLM images.

Ultraprecise single-molecule localization microscopy enables in situ distance measurements in intact cells.

Single-molecule localization microscopy (SMLM) has the potential to quantify the diversity in spatial arrangements of molecules in intact cells. However, this requires that the single-molecule emitters are localized with ultrahigh precision irrespective of the sample format and the length of the data acquisition. We advance SMLM to enable direct distance measurements between molecules in intact cells on the scale between 1 and 20 nm. Our actively stabilized microscope combines three-dimensional real-time drift corrections and achieves a stabilization of <1 nm and localization precision of ~1 nm. To demonstrate the biological applicability of the new microscope, we show a 4- to 7-nm difference in spatial separations between signaling T cell receptors and phosphatases (CD45) in active and resting T cells. In summary, by overcoming the major bottlenecks in SMLM imaging, it is possible to generate molecular images with nanometer accuracy and conduct distance measurements on the biological relevant length scales.

Can single molecule localization microscopy detect nanoclusters in T cells?

Nanoclusters of cell surface receptors have been detected with single molecule localization microscopy (SMLM) and are thought to mediate signal transduction. Clustering of the T cell receptor (TCR), for example, was reported to control signalling efficiency and antigen discrimination. However, the ability to detect nanoclusters with SMLM has been questioned. Here, we review the detection limits of SMLM as defined by both the physical limits and data processing, as well as evidence for nanoclusters arising from complementary techniques. We conclude with an outlook of how future data analysis can reveal the implications of molecular self-organization for signalling.

Determination of background, signal-to-noise, and dynamic range of a flow cytometer: A novel practical method for instrument characterization and standardization.

A well-defined scale calibration in flow cytometry can improve many aspects of data acquisition such as cytometer setup, instrument comparison and sample comparison. The theory for scale calibration was proposed by Steen over two decades ago, but it has never been put into regular use due to the lack of a widely available precision light source. The introduction of such a light source, the quantiFlashTM , gave this possibility. Here, we describe how this light source can be used to characterize a cytometer's PMT performance. We, therefore, characterized the instrument's response over the entire PMT voltage range. As a consequence, we propose a practical method to characterize a cytometer's signal-to-noise ratio (SNR) and dynamic range (DNR). This allows the selection of a voltage/gain corresponding to a PMT's maximum efficiency and hence the lowest electronic noise, which can help with experiment design. We further introduced a decibel (dB) scale for the presentation of SNR and DNR values. SNR and DNR are stand-alone values that allow the direct comparison of different instruments. Finally, with this method, it becomes clear that increased SNR comes at the expense of DNR and thus the limiting factor of modern cytometers is the DNR. © 2017 International Society for Advancement of Cytometry.

Multispectral flow cytometry: The consequences of increased light collection.

In recent years, multispectral flow cytometry systems have come to attention. They differ from conventional flow cytometers in two key ways: a multispectral flow cytometer collects the full spectral information at the single cell level and the detector configuration is fixed and not explicitly tuned to a particular staining panel. This brings about clear hardware advantages, as a closed system should be highly stable, and ease-of-use should be improved if used in conjunction with custom unmixing software. An open question remains: what are the benefits of multispectral over conventional flow cytometry in terms of sensitivity and resolution? To probe this, we use Q (detection efficiency) and B (background) values and develop a novel "multivariate population overlap factor" to characterize the cytometer performance. To verify the usefulness of our factor, we perform representative experiments and compare our overlap factor to Q and B. Finally, we conclude that the increased light collection of multispectral flow cytometry does indeed lead to increased sensitivity, an improved detection limit, and a higher resolution. © 2016 International Society for Advancement of Cytometry.

Complete Mitochondrial Complex I Deficiency Induces an Up-Regulation of Respiratory Fluxes That Is Abolished by Traces of Functional Complex I.

Complex I (NADH:ubiquinone oxidoreductase) is central to cellular NAD(+) recycling and accounts for approximately 40% of mitochondrial ATP production. To understand how complex I function impacts respiration and plant development, we isolated Arabidopsis (Arabidopsis thaliana) lines that lack complex I activity due to the absence of the catalytic subunit NDUFV1 (for NADH:ubiquinone oxidoreductase flavoprotein1) and compared these plants with ndufs4 (for NADH:ubiquinone oxidoreductase Fe-S protein4) mutants possessing trace amounts of complex I. Unlike ndufs4 plants, ndufv1 lines were largely unable to establish seedlings in the absence of externally supplied sucrose. Measurements of mitochondrial respiration and ATP synthesis revealed that compared with ndufv1, the complex I amounts retained by ndufs4 did not increase mitochondrial respiration and oxidative phosphorylation capacities. No major differences were seen in the mitochondrial proteomes, cellular metabolomes, or transcriptomes between ndufv1 and ndufs4. The analysis of fluxes through the respiratory pathway revealed that in ndufv1, fluxes through glycolysis and the tricarboxylic acid cycle were dramatically increased compared with ndufs4, which showed near wild-type-like fluxes. This indicates that the strong growth defects seen for plants lacking complex I originate from a switch in the metabolic mode of mitochondria and an up-regulation of respiratory fluxes. Partial reversion of these phenotypes when traces of active complex I are present suggests that complex I is essential for plant development and likely acts as a negative regulator of respiratory fluxes.

Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm.

MOTIVATION: The tried and true approach of flow cytometry data analysis is to manually gate on each biomarker separately, which is feasible for a small number of biomarkers, e.g. less than five. However, this rapidly becomes confusing as the number of biomarker increases. Furthermore, multivariate structure is not taken into account. Recently, automated gating algorithms have been implemented, all of which rely on unsupervised learning methodology. However, all unsupervised learning outputs suffer the same difficulties in validation in the absence of external knowledge, regardless of application domain. RESULTS: We present a new semi-automated algorithm for population discovery that is based on comparison to fluorescence-minus-one controls, thus transferring the problem into that of one-class classification, as opposed to being an unsupervised learning problem. The novel one-class classification algorithm is based on common principal components and can accommodate complex mixtures of multivariate densities. Computational time is short, and the simple nature of the calculations means the algorithm can easily be adapted to process large numbers of cells (10(6)). Furthermore, we are able to find rare cell populations as well as populations with low biomarker concentration, both of which are inherently hard to do in an unsupervised learning context without prior knowledge of the samples' composition. AVAILABILITY AND IMPLEMENTATION: R scripts are available via https://fccf.mpiib-berlin.mpg.de/daten/drfz/bioinformatics/with{username,password}={bioinformatics,Sar=Gac4}.

Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach.

Heterosis, the greater vigor of hybrids compared to their parents, has been exploited in maize breeding for more than 100 years to produce ever better performing elite hybrids of increased yield. Despite extensive research, the underlying mechanisms shaping the extent of heterosis are not well understood, rendering the process of selecting an optimal set of parental lines tedious. This study is based on a dataset consisting of 112 metabolite levels in young roots of four parental maize inbred lines and their corresponding twelve hybrids, along with the roots' biomass as a heterotic trait. Because the parental biomass is a poor predictor for hybrid biomass, we established a model framework to deduce the biomass of the hybrid from metabolite profiles of its parental lines. In the proposed framework, the hybrid metabolite levels are expressed relative to the parental levels by incorporating the standard concept of additivity/dominance, which we name the Combined Relative Level (CRL). Our modeling strategy includes a feature selection step on the parental levels which are demonstrated to be predictive of CRL across many hybrid metabolites. We demonstrate that these selected parental metabolites are further predictive of hybrid biomass. Our approach directly employs the diallel structure in a multivariate fashion, whereby we attempt to not only predict macroscopic phenotype (biomass), but also molecular phenotype (metabolite profiles). Therefore, our study provides the first steps for further investigations of the genetic determinants to metabolism and, ultimately, growth. Finally, our success on the small-scale experiments implies a valid strategy for large-scale experiments, where parental metabolite profiles may be used together with profiles of selected hybrids as a training set to predict biomass of all possible hybrids.

Exploring multicollinearity using a random matrix theory approach.

Clustering of gene expression data is often done with the latent aim of dimension reduction, by finding groups of genes that have a common response to potentially unknown stimuli. However, what is poorly understood to date is the behaviour of a low dimensional signal embedded in high dimensions. This paper introduces a multicollinear model which is based on random matrix theory results, and shows potential for the characterisation of a gene cluster's correlation matrix. This model projects a one dimensional signal into many dimensions and is based on the spiked covariance model, but rather characterises the behaviour of the corresponding correlation matrix. The eigenspectrum of the correlation matrix is empirically examined by simulation, under the addition of noise to the original signal. The simulation results are then used to propose a dimension estimation procedure of clusters from data. Moreover, the simulation results warn against considering pairwise correlations in isolation, as the model provides a mechanism whereby a pair of genes with `low' correlation may simply be due to the interaction of high dimension and noise. Instead, collective information about all the variables is given by the eigenspectrum.

Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells.

Gas chromatography-mass spectrometry (GC-MS) profiles were generated from U87 glioma cells and human mesenchymal stem cells (hMSC). 37 metabolites representing glycolysis intermediates, TCA cycle metabolites, amino acids and lipids were selected for a detailed analysis. The concentrations of these metabolites were compared and Pearson correlation coefficients were used to calculate the relationship between pairs of metabolites. Metabolite profiles and correlation patterns differ significantly between the two cell lines. These profiles can be considered as a signature of the underlying biochemical system and provide snap-shots of the metabolism in mesenchymal stem cells and tumor cells.

Assessing modularity using a random matrix theory approach.

Random matrix theory (RMT) is well suited to describing the emergent properties of systems with complex interactions amongst their constituents through their eigenvalue spectrums. Some RMT results are applied to the problem of clustering high dimensional biological data with complex dependence structure amongst the variables. It will be shown that a gene relevance or correlation network can be constructed by choosing a correlation threshold in a principled way, such that it corresponds to a block diagonal structure in the correlation matrix, if such a structure exists. The structure is then found using community detection algorithms, but with parameter choice guided by RMT predictions. The resulting clustering is compared to a variety of hierarchical clustering outputs and is found to the most generalised result, in that it captures all the features found by the other considered methods.