Cell Biology. Using cell-to-cell variability--a new era in molecular biology

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Multiscale chromatin dynamics and high entropy in plant iPSC ancestors

Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.

Physicochemical Perspective of Biological Heterogeneity

The vast majority of chemical processes that govern our lives occur within living cells. At the core of every life process, such as gene expression or metabolism, are chemical reactions that follow the fundamental laws of chemical kinetics and thermodynamics. Understanding these reactions and the factors that govern them is particularly important for the life sciences. The physicochemical environment inside cells, which can vary between cells and organisms, significantly impacts various biochemical reactions and increases the extent of population heterogeneity. This paper discusses using physical chemistry approaches for biological studies, including methods for studying reactions inside cells and monitoring their conditions. The potential for development in this field and possible new research areas are highlighted. By applying physical chemistry methodology to biochemistry in vivo, we may gain new insights into biology, potentially leading to new ways of controlling biochemical reactions.

Polymerase-Based Signal Delay for Temporally Regulating DNA Involved Reactions, Programming Dynamic Molecular Systems, and Biomimetic Sensing

Complex temporal molecular signals play a pivotal role in the intricate biological pathways of living organisms, and cells exhibit the ability to transmit and receive information by intricately managing the temporal dynamics of their signaling molecules. Although biomimetic molecular networks are successfully engineered outside of cells, the capacity to precisely manipulate temporal behaviors remains limited. In this study, the catalysis activity of isothermal DNA polymerase (DNAP) through combined use of molecular dynamics simulation analysis and fluorescence assays is first characterized. DNAP-driven delay in signal strand release ranged from 100 to 102 min, which is achieved through new strategies including the introduction of primer overhangs, utilization of inhibitory reagents, and alteration of DNA template lengths. The results provide a deeper insight into the underlying mechanisms of temporal control DNAP-mediated primer extension and DNA strand displacement reactions. Then, the regulated DNAP catalysis reactions are applied in temporal modulation of downstream DNA-involved reactions, the establishment of dynamic molecular signals, and the generation of barcodes for multiplexed detection of target genes. The utility of DNAP-based signal delay as a dynamic DNA nanotechnology extends beyond theoretical concepts and achieves practical applications in the fields of cell-free synthetic biology and bionic sensing.

Weed biology and management in the multi-omics era: Progress and perspectives

Weeds pose a significant threat to crop production, resulting in substantial yield reduction. In addition, they possess robust weedy traits that enable them to survive in extreme environments and evade human control. In recent years, the application of multi-omics biotechnologies has helped to reveal the molecular mechanisms underlying these weedy traits. In this review, we systematically describe diverse applications of multi-omics platforms for characterizing key aspects of weed biology, including the origins of weed species, weed classification, and the underlying genetic and molecular bases of important weedy traits such as crop-weed interactions, adaptability to different environments, photoperiodic flowering responses, and herbicide resistance. In addition, we discuss limitations to the application of multi-omics techniques in weed science, particularly compared with their extensive use in model plants and crops. In this regard, we provide a forward-looking perspective on the future application of multi-omics technologies to weed science research. These powerful tools hold great promise for comprehensively and efficiently unraveling the intricate molecular genetic mechanisms that underlie weedy traits. The resulting advances will facilitate the development of sustainable and highly effective weed management strategies, promoting greener practices in agriculture.

Living Cell as a Self-Synchronized Chemical Reactor

Thermal fluctuations power all processes inside living cells. Therefore, these processes are inherently random. However, myriad multistep chemical reactions act in concerto inside a cell, finally leading to this chemical reactor's self-replication. We speculate that an underlying mechanism in nature must exist that allows all of these reactions to synchronize at multiple time and length scales, overcoming in this way the random nature of any single process in a cell. This Perspective discusses what type of research is needed to understand this undiscovered synchronization law.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

The multiple activations in budding yeast S-phase checkpoint are Poisson processes

Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.

Characterizing Intraindividual Podocyte Morphology In Vitro with Different Innovative Microscopic and Spectroscopic Techniques

Podocytes are critical components of the glomerular filtration barrier, sitting on the outside of the glomerular basement membrane. Primary and secondary foot processes are characteristic for podocytes, but cell processes that develop in culture were not studied much in the past. Moreover, protocols for diverse visualization methods mostly can only be used for one technique, due to differences in fixation, drying and handling. However, we detected by single-cell RNA sequencing (scRNAseq) analysis that cells reveal high variability in genes involved in cell type-specific morphology, even within one cell culture dish, highlighting the need for a compatible protocol that allows measuring the same cell with different methods. Here, we developed a new serial and correlative approach by using a combination of a wide variety of microscopic and spectroscopic techniques in the same cell for a better understanding of podocyte morphology. In detail, the protocol allowed for the sequential analysis of identical cells with light microscopy (LM), Raman spectroscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). Skipping the fixation and drying process, the protocol was also compatible with scanning ion-conductance microscopy (SICM), allowing the determination of podocyte surface topography of nanometer-range in living cells. With the help of nanoGPS Oxyo^®, tracking concordant regions of interest of untreated podocytes and podocytes stressed with TGF-β were analyzed with LM, SEM, Raman spectroscopy, AFM and SICM, and revealed significant morphological alterations, including retraction of podocyte process, changes in cell surface morphology and loss of cell-cell contacts, as well as variations in lipid and protein content in TGF-β treated cells. The combination of these consecutive techniques on the same cells provides a comprehensive understanding of podocyte morphology. Additionally, the results can also be used to train automated intelligence networks to predict various outcomes related to podocyte injury in the future.

Interplay of Fluid Mechanics and Matrix Stiffness in Tuning the Mechanical Behaviors of Single Cells Probed by Atomic Force Microscopy

It is well known that both fluid mechanics and matrix stiffness present within the cellular microenvironments play an essential role in the physiological and pathological processes of cells. However, so far, knowledge of the interplay of fluid mechanics and matrix stiffness in tuning the mechanical behaviors of single cells is still extremely limited. Particularly, atomic force microscopy (AFM) is now an important and standard tool for characterizing the mechanical properties of single living cells. Nevertheless, studies of utilizing AFM to detect cellular mechanics are commonly performed in static medium conditions, which are unable to access the effects of fluidic media on cellular behaviors. Here, by integrating AFM with a fluidic cell medium device and hydrogel technology, the combined effects of fluid mechanics and matrix stiffness on cell mechanics were investigated. A fluidic medium device with tunable fluid mechanics was established to simulate the shear flow effects, and hydrogels were used to fabricate substrates with different stiffnesses for cell growth. Especially, the cantilever of the AFM probe was modified with a microsphere to indent cells for probing cell mechanics. Based on the established experimental platform, the elastic and viscous properties of single living cells grown on substrates with tunable matrix stiffness under fluidic microenvironments were quantitatively measured, and the remarkable alterations in the mechanical properties of cells were unraveled. The subcellular structure changes of cells in fluidic microenvironments were observed by fluorescence microscopy. Further, AFM morphological imaging was used to image living cells grown in fluidic medium conditions, and significant changes in the surface structure and roughness of cells were observed. The study provides a novel way to investigate the synergistic effects of fluid mechanics and matrix stiffness on the behaviors of single cells, which will benefit unveiling the underlying mechanical cues involved the interactions between microenvironments and cells.

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

MOTIVATION

Cell-cell communications regulate internal cellular states, e.g. gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation of cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types.

RESULTS

Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advances and applications of single-cell omics technologies in plant research

Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.

Bayesian Inference of Stochastic Dynamic Models Using Early-Rejection Methods Based on Sequential Stochastic Simulations

Stochastic modelling is an important method to investigate the functions of noise in a wide range of biological systems. However, the parameter inference for stochastic models is still a challenging problem partially due to the large computing time required for stochastic simulations. To address this issue, we propose a novel early-rejection method by using sequential stochastic simulations. We first show that a large number of stochastic simulations are required to obtain reliable inference results. Instead of generating a large number of simulations for each parameter sample, we propose to generate these simulations in a number of stages. The simulation process will go to the next stage only if the accuracy of simulations at the current stage satisfies a given error criterion. We propose a formula to determine the error criterion and use a stochastic differential equation model to examine the effects of different criteria. Three biochemical network models are used to evaluate the efficiency and accuracy of the proposed method. Numerical results suggest the proposed early-rejection method achieves substantial improvement in the efficiency for the inference of stochastic models.

Sphingolipids control dermal fibroblast heterogeneity

Human cells produce thousands of lipids that change during cell differentiation and can vary across individual cells of the same type. However, we are only starting to characterize the function of these cell-to-cell differences in lipid composition. Here, we measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging with single-cell transcriptomics. We found that the cell-to-cell variations of specific lipid metabolic pathways contribute to the establishment of cell states involved in the organization of skin architecture. Sphingolipid composition is shown to define fibroblast subpopulations, with sphingolipid metabolic rewiring driving cell-state transitions. Therefore, cell-to-cell lipid heterogeneity affects the determination of cell states, adding a new regulatory component to the self-organization of multicellular systems.

Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

Single-cell analysis for the study of viral inhibitors

Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.

Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, a graded increase in survival of colony-forming unit-erythroid (CFU-E) cells is observed upon stimulation with erythropoietin (Epo). To identify components of Janus kinase 2/signal transducer and activator of transcription 5 (JAK2/STAT5) signal transduction that contribute to the graded population response, we extended a cell-population-level model calibrated with experimental data to study the behavior in single cells. The single-cell model shows that the high cell-to-cell variability in nuclear phosphorylated STAT5 is caused by variability in the amount of Epo receptor (EpoR):JAK2 complexes and of SHP1, as well as the extent of nuclear import because of the large variance in the cytoplasmic volume of CFU-E cells. 24-118 pSTAT5 molecules in the nucleus for 120 min are sufficient to ensure cell survival. Thus, variability in membrane-associated processes is sufficient to convert a switch-like behavior at the single-cell level to a graded population-level response.

Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis

In traditional sequencing techniques, the different functions of cells and the different roles they play in differentiation are often ignored. With the advancement of single-cell RNA sequencing (scRNA-seq) techniques, scientists can measure the gene expression value at the single-cell level, and it is helping to understand the heterogeneity hidden in cells. One of the most powerful ways to find heterogeneity is using the unsupervised clustering method to get separate subpopulations. In this paper, we propose a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. We test the validity of our proposed method on five scRNA-seq datasets. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies. Therefore, we can conclude that SDCNMF is effective in scRNA-seq data analysis. This paper proposes a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies.

SpaceM reveals metabolic states of single cells

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

The Combination of Single-Cell and Next-Generation Sequencing Can Reveal Mosaicism for BRCA2 Mutations and the Fine Molecular Details of Tumorigenesis

Germline mutations in the BRCA1 and BRCA2 genes are responsible for hereditary breast and ovarian cancer syndrome. Germline and somatic BRCA1/2 mutations may define therapeutic targets and refine cancer treatment options. However, routine BRCA diagnostic approaches cannot reveal the exact time and origin of BRCA1/2 mutation formation, and thus, the fine details of their contribution to tumor progression remain less clear. Here, we establish a diagnostic pipeline using high-resolution microscopy and laser microcapture microscopy to test for BRCA1/2 mutations in the tumor at the single-cell level, followed by deep next-generation sequencing of various tissues from the patient. To demonstrate the power of our approach, here, we describe a detailed single-cell-level analysis of an ovarian cancer patient we found to exhibit constitutional somatic mosaicism of a pathogenic BRCA2 mutation. Employing next-generation sequencing, BRCA2 c.7795G>T, p.(Glu2599Ter) was detected in 78% of reads in DNA extracted from ovarian cancer tissue and 25% of reads in DNA derived from peripheral blood, which differs significantly from the expected 50% of a hereditary mutation. The BRCA2 mutation was subsequently observed at 17-20% levels in the normal ovarian and buccal tissue of the patient. Together, our findings suggest that this mutation occurred early in embryonic development. Characterization of the mosaic mutation at the single-cell level contributes to a better understanding of BRCA mutation formation and supports the concept that the combination of single-cell and next-generation sequencing methods is advantageous over traditional mutational analysis methods. This study is the first to characterize constitutional mosaicism down to the single-cell level, and it demonstrates that BRCA2 mosaicism occurring early during embryogenesis can drive tumorigenesis in ovarian cancer.

scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy

Background

Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation.

Result

In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools.

Conclusion

Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04136-1.

Single-cell fucosylation breakdown: Switching fucose to europium

Fucosylation and its fucosidic linkage-specific motifs are believed to be essential to understand their distinct roles in cellular behavior, but their quantitative information has not yet been fully disclosed due to the requirements of ultra-sensitivity and selectivity. Herein, we report an approach that converts fucose (Fuc) to stable europium (Eu) isotopic mass signal on hard ionization inductively coupled plasma mass spectrometry (ICP-MS). Metabolically assembled azido-fucose on the cell surface allows us to tag them with an alkyne-customized Eu-crafted bacteriophage MS2 capsid nanoparticle for Eu signal multiplication, resulting in an ever lowest detection limit of 4.2 zmol Fuc. Quantitative breakdown of the linkage-specific fucosylation motifs in situ preserved on single cancerous HepG2 and paracancerous HL7702 cells can thus be realized on a single-cell ICP-MS platform, specifying their roles during the cancering process. This approach was further applied to the discrimination of normal hepatocellular cells and highly, moderately, and poorly differentiated hepatoma cells collected from real hepatocellular carcinoma tissues.

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Switching facile fucose to stable Eu mass signal on a single-cell ICP-MS platform

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Ever lowest LOD of 4.2 zmol FucAz was achieved using a Eu-decorated MS2 nanoparticle

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Single-cell breakdown of fucosidic linkage-specific motifs

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Discrimination of highly, moderately, and poorly differentiated HCC from normal ones

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Dynamics of Whole-Genome Contacts of Nucleoli in Drosophila Cells Suggests a Role for rDNA Genes in Global Epigenetic Regulation

Chromosomes are organized into 3D structures that are important for the regulation of gene expression and differentiation. Important role in formation of inter-chromosome contacts play rDNA clusters that make up nucleoli. In the course of differentiation, heterochromatization of rDNA units in mouse cells is coupled with the repression or activation of different genes. Furthermore, the nucleoli of human cells shape the direct contacts with genes that are involved in differentiation and cancer. Here, we identified and categorized the genes located in the regions where rDNA clusters make frequent contacts. Using a 4C approach, we demonstrate that in Drosophila S2 cells, rDNA clusters form contacts with genes that are involved in chromosome organization and differentiation. Heat shock treatment induces changes in the contacts between nucleoli and hundreds of genes controlling morphogenesis. We show that nucleoli form contacts with regions that are enriched with active or repressive histone marks and where small non-coding RNAs are mapped. These data indicate that rDNA contacts are involved in the repression and activation of gene expression and that rDNA clusters orchestrate large groups of Drosophila genes involved in differentiation.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

Large field-of-view scanning small-angle X-ray scattering of mammalian cells

X-ray imaging is a complementary method to electron and fluorescence microscopy for studying biological cells. In particular, scanning small-angle X-ray scattering provides overview images of whole cells in real space as well as local, high-resolution reciprocal space information, rendering it suitable to investigate subcellular nanostructures in unsliced cells. One persisting challenge in cell studies is achieving high throughput in reasonable times. To this end, a fast scanning mode is used to image hundreds of cells in a single scan. A way of dealing with the vast amount of data thus collected is suggested, including a segmentation procedure and three complementary kinds of analysis, i.e. characterization of the cell population as a whole, of single cells and of different parts of the same cell. The results show that short exposure times, which enable faster scans and reduce radiation damage, still yield information in agreement with longer exposure times.

Nanoelectrochemical biosensors for monitoring ROS in cancer cells

A novel strategy has been constructed based on a SiC@C nanowire electrode for intracellular electrochemical analysis to monitor ROS levels in cancer or tumor cells.

Single-Cell Expression Variability Implies Cell Function

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis

Cells sense and respond to signals in their local environment by activating signaling cascades that lead to phenotypic changes. Differences in these signals can be discriminated at the population level; however, single cells have been thought to be limited in their capacity to distinguish ligand doses due to signaling noise. We describe here the rational development of a genetically encoded FoxO1 sensor, which serves as a down-stream readout of insulin growth factor-phosphatidylinositol 3-kinase IGF-PI3K-AKT signaling pathway activity. With this reporter, we tracked individual cell responses to multiple IGF-I doses, pathway inhibitors, and repeated treatments. We observed that individual cells can discriminate multiple IGF-I doses, and these responses are sustained over time, are reproducible at the single-cell level, and display cell-to-cell heterogeneity. These studies imply that cell-to-cell variation in signaling responses is biologically meaningful and support the endeavor to elucidate mechanisms of cell signaling at the level of the individual cell.

Human gene expression variability and its dependence on methylation and aging

BACKGROUND

Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood.

RESULTS

Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated.

CONCLUSIONS

We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.

rDNA Clusters Make Contact with Genes that Are Involved in Differentiation and Cancer and Change Contacts after Heat Shock Treatment

Human rDNA clusters form numerous contacts with different chromosomal regions as evidenced by chromosome conformation capture data. Heterochromatization of rDNA genes leads to heterochromatization in different chromosomal regions coupled with the activation of the transcription of genes related to differentiation. These data suggest a role for rDNA clusters in the regulation of many human genes. However, the genes that reside within the rDNA-contacting regions have not been identified. The purpose of this study was to detect and characterize the regions where rDNA clusters make frequent contacts and to identify and categorize genes located in these regions. We analyzed the regions that contact rDNA using 4C data and show that these regions are enriched with genes specifying transcription factors and non-coding RNAs involved in differentiation and development. The rDNA-contacting genes are involved in neuronal development and are associated with different cancers. Heat shock treatment led to dramatic changes in the pattern of rDNA-contacting sites, especially in the regions possessing long stretches of H3K27ac marks. Whole-genome analysis of rDNA-contacting sites revealed specific epigenetic marks and the transcription sites of 20–100 nt non-coding RNAs in these regions. The rDNA-contacting genes jointly regulate many genes that are involved in the control of transcription by RNA polymerase II and the development of neurons. Our data suggest a role for rDNA clusters in the differentiation of human cells and carcinogenesis.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

To understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

Spectral clustering based on learning similarity matrix

Motivation

Single-cell RNA-sequencing (scRNA-seq) technology can generate genome-wide expression data at the single-cell levels. One important objective in scRNA-seq analysis is to cluster cells where each cluster consists of cells belonging to the same cell type based on gene expression patterns.

Results

We introduce a novel spectral clustering framework that imposes sparse structures on a target matrix. Specifically, we utilize multiple doubly stochastic similarity matrices to learn a similarity matrix, motivated by the observation that each similarity matrix can be a different informative representation of the data. We impose a sparse structure on the target matrix followed by shrinking pairwise differences of the rows in the target matrix, motivated by the fact that the target matrix should have these structures in the ideal case. We solve the proposed non-convex problem iteratively using the ADMM algorithm and show the convergence of the algorithm. We evaluate the performance of the proposed clustering method on various simulated as well as real scRNA-seq data, and show that it can identify clusters accurately and robustly.

Availability and implementation

The algorithm is implemented in MATLAB. The source code can be downloaded at https://github.com/ishspsy/project/tree/master/MPSSC.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

Nomenclature and heterogeneity: consequences for the use of mesenchymal stem cells in regenerative medicine

Mesenchymal stem cells (MSCs) are in development for many clinical indications, based both on ‘stem’ properties (tissue repair or regeneration) and on signaling repertoire (immunomodulatory and anti-inflammatory effects). Potential conflation of MSC properties with those of tissue-derived stromal cells presents difficulties in comparing study outcomes and represents a source of confusion in cell therapy development. Cultured MSCs demonstrate significant heterogeneity in clonogenicity and multi-lineage differentiation potential. However in vivo biology of MSCs includes native functions unrelated to regenerative medicine applications, so do nomenclature and heterogeneity matter? In this perspective we examine some consequences of the nomenclature debate and heterogeneity of MSCs. Regulatory expectations are considered, emphasizing that product development should prioritize detailed characterization of therapeutic cell populations for specific indications.

Capturing single-cell heterogeneity via data fusion improves image-based profiling

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features’ dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound’s mechanism of action (MoA) and a gene’s pathway.

A challenge with single-cell resolution methods is that cell heterogeneity should be captured while allowing for comparisons between populations. Here the authors fuse information from the dispersion profiles with the average profiles at the level of profiles’ similarity matrices for single cell imaging data.

Magnesium Flux Modulates Ribosomes to Increase Bacterial Survival

Bacteria exhibit cell-to-cell variability in their resilience to stress, for example, following antibiotic exposure. Higher resilience is typically ascribed to "dormant" non-growing cellular states. Here, by measuring membrane potential dynamics of Bacillus subtilis cells, we show that actively growing bacteria can cope with ribosome-targeting antibiotics through an alternative mechanism based on ion flux modulation. Specifically, we observed two types of cellular behavior: growth-defective cells exhibited a mathematically predicted transient increase in membrane potential (hyperpolarization), followed by cell death, whereas growing cells lacked hyperpolarization events and showed elevated survival. Using structural perturbations of the ribosome and proteomic analysis, we uncovered that stress resilience arises from magnesium influx, which prevents hyperpolarization. Thus, ion flux modulation provides a distinct mechanism to cope with ribosomal stress. These results suggest new approaches to increase the effectiveness of ribosome-targeting antibiotics and reveal an intriguing connection between ribosomes and the membrane potential, two fundamental properties of cells.

Effect of cell sample size in atomic force microscopy nanoindentation

Single-cell technologies are powerful tools to evaluate cell characteristics. In particular, Atomic Force Microscopy (AFM) nanoindentation experiments have been widely used to study single cell mechanical properties. One important aspect related to single cell techniques is the need for sufficient statistical power to obtain reliable results. This aspect is often overlooked in AFM experiments were sample sizes are arbitrarily set. The aim of the present work was to propose a tool for sample size estimation in the context of AFM nanoindentation experiments of single cell. To this aim, a retrospective approach was used by acquiring a large dataset of experimental measurements on four bone cell types and by building saturation curves for increasing sample sizes with a bootstrap resampling method. It was observed that the coefficient of variation (CV_%) decayed with a function of the form y = ax^b with similar parameters for all samples tested and that sample sizes of 21 and 83 cells were needed for the specific cells and protocol employed if setting a maximum threshold on CV_% of 10% or 5%, respectively. The developed tool is made available as an open-source repository and guidelines are provided for its use for AFM nanoindentation experimental design.

Fluctuations in cell density alter protein markers of multiple cellular compartments, confounding experimental outcomes

The life cycle of cultured proliferating cells is characterized by fluctuations in cell population density induced by periodic subculturing. This leads to corresponding changes in micro- and macroenvironment of the cells, accompanied by altered cellular metabolism, growth rate and locomotion. Studying cell density-dependent morphological, physiological and biochemical fluctuations is relevant for understanding basic cellular mechanisms and for uncovering the intrinsic variation of commonly used tissue culture experimental models. Using multiple cell lines, we found that expression levels of the autophagic markers p62 and LC3II, and lysosomal enzyme cathepsin D were altered in highly confluent cells as a consequence of nutrient depletion and cell crowding, which led to inactivation of the mTOR signaling pathway. Furthermore, both Lamp1 and active focal adhesion kinase (FAK) were reduced in high-density cells, while chemical inhibition or deletion of FAK led to alterations in lysosomal and autophagic proteins, as well as in the mTOR signaling. This was accompanied by alterations in the Hippo signaling pathway, while cell cycle checkpoint regulator p-cdc2 remained unaffected in at least one studied cell line. On the other hand, allometric scaling of cellular compartments in growing cell populations resulted in biochemically detectable changes in the plasma membrane proteins Na⁺K⁺-ATPase and cadherin, and nuclear proteins HDAC1 and Lamin B1. Finally, we demonstrate how treatment-induced changes in cell density and corresponding modulation of susceptible proteins may lead to ambiguous experimental outcomes, or erroneous interpretation of cell culture data. Together, our data emphasize the need to recognize cell density as an important experimental variable in order to improve scientific rigor of cell culture-based studies.

Context-explorer: Analysis of spatially organized protein expression in high-throughput screens

A growing body of evidence highlights the importance of the cellular microenvironment as a regulator of phenotypic and functional cellular responses to perturbations. We have previously developed cell patterning techniques to control population context parameters, and here we demonstrate context-explorer (CE), a software tool to improve investigation cell fate acquisitions through community level analyses. We demonstrate the capabilities of CE in the analysis of human and mouse pluripotent stem cells (hPSCs, mPSCs) patterned in colonies of defined geometries in multi-well plates. CE employs a density-based clustering algorithm to identify cell colonies. Using this automatic colony classification methodology, we reach accuracies comparable to manual colony counts in a fraction of the time, both in micropatterned and unpatterned wells. Classifying cells according to their relative position within a colony enables statistical analysis of spatial organization in protein expression within colonies. When applied to colonies of hPSCs, our analysis reveals a radial gradient in the expression of the transcription factors SOX2 and OCT4. We extend these analyses to colonies of different sizes and shapes and demonstrate how the metrics derived by CE can be used to asses the patterning fidelity of micropatterned plates. We have incorporated a number of features to enhance the usability and utility of CE. To appeal to a broad scientific community, all of the software’s functionality is accessible from a graphical user interface, and convenience functions for several common data operations are included. CE is compatible with existing image analysis programs such as CellProfiler and extends the analytical capabilities already provided by these tools. Taken together, CE facilitates investigation of spatially heterogeneous cell populations for fundamental research and drug development validation programs.

Cell behavior is influenced by cues that cells receive from their surrounding environment such as signals secreted from other cells and cell-to-cell contact. These factors are spatially heterogeneous and cells at different positions within a colony will experience varying degrees of influence from such environmental cues. In vitro assays often do not allow control over environmental variables and there is a lack of easy to use software to investigate the effect of spatial variation in these factors. We have developed a software package to address this gap and facilitate the quantification of spatially heterogeneous cell responses. Our software accurately identifies colonies of cells within a well and individual cells can be grouped according to their position within these colonies, which enables quantification of cell response as a function of cellular location. To support broad scientific accessibility, the full functionality of the software is available through a graphical user interface. Using this software to analyze data from a screening-optimized micropatterning platform, we show that human pluripotent stem cell-derived colonies grown either under pluripotency maintenance or differentiation-inducing conditions exhibit cell responses that are dependent on spatial organization. This technology should enable more accurate and predictive context-dependent drug screening and cell-fate investigation.

Advanced Nanoscale Approaches to Single-(Bio)entity Sensing and Imaging

Individual (bio)chemical entities could show a very heterogeneous behaviour under the same conditions that could be relevant in many biological processes of significance in the life sciences. Conventional detection approaches are only able to detect the average response of an ensemble of entities and assume that all entities are identical. From this perspective, important information about the heterogeneities or rare (stochastic) events happening in individual entities would remain unseen. Some nanoscale tools present interesting physicochemical properties that enable the possibility to detect systems at the single-entity level, acquiring richer information than conventional methods. In this review, we introduce the foundations and the latest advances of several nanoscale approaches to sensing and imaging individual (bio)entities using nanoprobes, nanopores, nanoimpacts, nanoplasmonics and nanomachines. Several (bio)entities such as cells, proteins, nucleic acids, vesicles and viruses are specifically considered. These nanoscale approaches provide a wide and complete toolbox for the study of many biological systems at the single-entity level.

Distinct antiviral signatures revealed by the magnitude and round of influenza virus replication in vivo

Influenza virus has a broad cellular tropism in the respiratory tract. Infected epithelial cells sense the infection and initiate an antiviral response. To define the antiviral response at the earliest stages of infection we used a series of single-cycle reporter viruses. These viral probes demonstrated cells in vivo harbor a range in magnitude of virus replication. Transcriptional profiling of cells supporting different levels of replication revealed tiers of IFN-stimulated gene expression. Uninfected cells and cells with blunted replication expressed a distinct and potentially protective antiviral signature, while cells with high replication expressed a unique reserve set of antiviral genes. Finally, we used these single-cycle reporter viruses to determine the antiviral landscape during virus spread, which unveiled disparate protection of epithelial cell subsets mediated by IFN in vivo. Together these results highlight the complexity of virus-host interactions within the infected lung and suggest that magnitude and round of replication tune the antiviral response.

Measuring cell cycle-dependent DNA damage responses and p53 regulation on a cell-by-cell basis from image analysis

DNA damage in cells occurs from both endogenous and exogenous sources, and failure to repair such damage is associated with the emergence of different cancers, neurological disorders and aging. DNA damage responses (DDR) in cells are closely associated with the cell cycle. While most of our knowledge of DDR comes from bulk biochemistry, such methods require cells to be arrested at specific stages for cell cycle studies, potentially altering measured responses; nor is cell to cell variability in DDR or direct cell-level correlation of two response metrics measured in such methods. To overcome these limitations we developed a microscopy-based assay for determining cell cycle stages over large cell numbers. This method can be used to study cell-cycle-dependent DDR in cultured cells without the need for cell synchronization. Upon DNA damage γH2A.X induction was correlated to nuclear enrichment of p53 on a cell-by-cell basis and in a cell cycle dependent manner. Imaging-based cell cycle staging was combined with single molecule P53 mRNA detection and immunofluorescence for p53 protein in the very same cells to reveal an intriguing repression of P53 transcript numbers due to reduced transcription across different stages of the cell cycle during DNA damage. Our study hints at an unexplored mechanism for p53 regulation and underscores the importance of measuring single cell level responses to DNA damage.

Tracing the origin of heterogeneity and symmetry breaking in the early mammalian embryo

A fundamental question in developmental and stem cell biology concerns the origin and nature of signals that initiate asymmetry leading to pattern formation and self-organization. Instead of having prominent pre-patterning determinants as present in model organisms (worms, sea urchin, frog), we propose that the mammalian embryo takes advantage of more subtle cues such as compartmentalized intracellular reactions that generate micro-scale inhomogeneity, which is gradually amplified over several cellular generations to drive pattern formation while keeping developmental plasticity. It is therefore possible that by making use of compartmentalized information followed by its amplification, mammalian embryos would follow general principle of development found in other organisms in which the spatial cue is more robustly presented.

Versatile approach for functional analysis of human proteins and efficient stable cell line generation using FLP-mediated recombination system

Deciphering a function of a given protein requires investigating various biological aspects. Usually, the protein of interest is expressed with a fusion tag that aids or allows subsequent analyses. Additionally, downregulation or inactivation of the studied gene enables functional studies. Development of the CRISPR/Cas9 methodology opened many possibilities but in many cases it is restricted to non-essential genes. Recombinase-dependent gene integration methods, like the Flp-In system, are very good alternatives. The system is widely used in different research areas, which calls for the existence of compatible vectors and efficient protocols that ensure straightforward DNA cloning and generation of stable cell lines. We have created and validated a robust series of 52 vectors for streamlined generation of stable mammalian cell lines using the FLP recombinase-based methodology. Using the sequence-independent DNA cloning method all constructs for a given coding-sequence can be made with just three universal PCR primers. Our collection allows tetracycline-inducible expression of proteins with various tags suitable for protein localization, FRET, bimolecular fluorescence complementation (BiFC), protein dynamics studies (FRAP), co-immunoprecipitation, the RNA tethering assay and cell sorting. Some of the vectors contain a bidirectional promoter for concomitant expression of miRNA and mRNA, so that a gene can be silenced and its product replaced by a mutated miRNA-insensitive version. Our toolkit and protocols have allowed us to create more than 500 constructs with ease. We demonstrate the efficacy of our vectors by creating stable cell lines with various tagged proteins (numatrin, fibrillarin, coilin, centrin, THOC5, PCNA). We have analysed transgene expression over time to provide a guideline for future experiments and compared the effectiveness of commonly used inducers for tetracycline-responsive promoters. As proof of concept we examined the role of the exoribonuclease XRN2 in transcription termination by RNAseq.

Modeling of cytometry data in logarithmic space: When is a bimodal distribution not bimodal?

Recent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements. © 2018 International Society for Advancement of Cytometry.