Characteristics Of Individual Cells Research Articles

Single-cell profiling of tumour evolution in multiple myeloma - opportunities for precision medicine.

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.

ScNAME: neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data.

The rapid development of single-cell RNA sequencing (scRNA-seq) makes it possible to study the heterogeneity of individual cell characteristics. Cell clustering is a vital procedure in scRNA-seq analysis, providing insight into complex biological phenomena. However, the noisy, high-dimensional and large-scale nature of scRNA-seq data introduces challenges in clustering analysis. Up to now, many deep learning-based methods have emerged to learn underlying feature representations while clustering. However, these methods are inefficient when it comes to rare cell type identification and barely able to fully utilize gene dependencies or cell similarity integrally. As a result, they cannot detect a clear cell type structure which is required for clustering accuracy as well as downstream analysis. Here, we propose a novel scRNA-seq clustering algorithm called scNAME which incorporates a mask estimation task for gene pertinence mining and a neighborhood contrastive learning framework for cell intrinsic structure exploitation. The learned pattern through mask estimation helps reveal uncorrupted data structure and denoise the original single-cell data. In addition, the randomly created augmented data introduced in contrastive learning not only helps improve robustness of clustering, but also increases sample size in each cluster for better data capacity. Beyond this, we also introduce a neighborhood contrastive paradigm with an offline memory bank, global in scope, which can inspire discriminative feature representation and achieve intra-cluster compactness, yet inter-cluster separation. The combination of mask estimation task, neighborhood contrastive learning and global memory bank designed in scNAME is conductive to rare cell type detection. The experimental results of both simulations and real data confirm that our method is accurate, robust and scalable. We also implement biological analysis, including marker gene identification, gene ontology and pathway enrichment analysis, to validate the biological significance of our method. To the best of our knowledge, we are among the first to introduce a gene relationship exploration strategy, as well as a global cellular similarity repository, in the single-cell field. An implementation of scNAME is available from https://github.com/aster-ww/scNAME. Supplementary data are available at Bioinformatics online.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

BackgroundTime-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.ResultsWe present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.ConclusionsViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Cell Systems Bioelectricity: How Different Intercellular Gap Junctions Could Regionalize a Multicellular Aggregate.

Simple SummaryElectric potential patterns across tissues are instructive for development, regeneration, and tumorigenesis because they can influence transcription, migration, and differentiation through biochemical and biomechanical downstream processes. Determining the origins of the spatial domains of distinct potential, which in turn decide anatomical features such as limbs, eyes, brain, and heart, is critical to a mature understanding of how bioelectric signaling drives morphogenesis. We studied theoretically how connexin proteins with different voltage-gated gap junction conductances can maintain multicellular regions at distinct membrane potentials. We analyzed a minimal model that incorporates effective conductances ultimately related to specific ion channel and junction proteins that are amenable to external regulation. We also consider a bioelectrical relationship between the connexin composition of the intercellular gap junction and different stages of cancer.Electric potential distributions can act as instructive pre-patterns for development, regeneration, and tumorigenesis in cell systems. The biophysical states influence transcription, proliferation, cell shape, migration, and differentiation through biochemical and biomechanical downstream transduction processes. A major knowledge gap is the origin of spatial patterns in vivo, and their relationship to the ion channels and the electrical synapses known as gap junctions. Understanding this is critical for basic evolutionary developmental biology as well as for regenerative medicine. We computationally show that cells may express connexin proteins with different voltage-gated gap junction conductances as a way to maintain multicellular regions at distinct membrane potentials. We show that increasing the multicellular connectivity via enhanced junction function does not always contribute to the bioelectrical normalization of abnormally depolarized multicellular patches. From a purely electrical junction view, this result suggests that the reduction rather than the increase of specific connexin levels can also be a suitable bioelectrical approach in some cases and time stages. We offer a minimum model that incorporates effective conductances ultimately related to specific ion channel and junction proteins that are amenable to external regulation. We suggest that the bioelectrical patterns and their encoded instructive information can be externally modulated by acting on the mean fields of cell systems, a complementary approach to that of acting on the molecular characteristics of individual cells. We believe that despite the limitations of a biophysically focused model, our approach can offer useful qualitative insights into the collective dynamics of cell system bioelectricity.

A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.

Traditional bulk RNA-sequencing of human pancreatic islets mainly reflects transcriptional response of major cell types. Single-cell RNA sequencing technology enables transcriptional characterization of individual cells, and thus makes it possible to detect cell types and subtypes. To tackle the heterogeneity of single-cell RNA-seq data, powerful and appropriate clustering is required to facilitate the discovery of cell types. In this paper, we propose a new clustering framework based on a graph-based model with various types of dissimilarity measures. We take the compositional nature of single-cell RNA-seq data into account and employ log-ratio transformations. The practical merit of the proposed method is demonstrated through the application to the centered log-ratio-transformed single-cell RNA-seq data for human pancreatic islets. The practical merit is also demonstrated through comparisons with existing single-cell clustering methods. The R-package for the proposed method can be found at https://github.com/Zhang-Data-Science-Research-Lab/LrSClust.

EHooke: A tool for automated image analysis of spherical bacteria based on cell cycle progression.

Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.

Adaptive architecture and mechanoresponse of epithelial cells on a torus

Curvature is a geometric feature widely observed in the epithelia and critical to the performance of fundamental biological functions. Understanding curvature-related biophysical phenomena remains challenging partly owing to the difficulty of quantitatively tuning and measuring curvatures of interfacing individual cells. In this study, we prepared confluent wild-type Madin–Darby canine kidney cells on a torus structure presenting positive, zero, and negative Gaussian curvatures with a tubule diameter of 2–7 cells and quantified the mechanobiological characteristics of individual cells. Cells on the torus surface exhibited topological sensing ability both as an individual cell and collective cell organization. Both cell bodies and nuclei, adapted on the torus, exhibited local Gaussian curvature-dependent preferential orientation. The cells on the torus demonstrated significant adjustment in the nuclear area and exhibited asymmetric nuclear position depending on the local Gaussian curvature. Moreover, cells on top of the torus, where local Gaussian curvature is near zero, exhibited more sensitive morphological adaptations than the nuclei depending on the Gaussian curvature gradient. Furthermore, the spatial heterogeneity of intermediate filament proteins related to mechanoresponsive expression of the cell body and nucleus, vimentin, keratin and lamin A, revealed local Gaussian curvature as a key factor of cellular adaptation on curved surfaces.

Comparative Study of the Electrical Parameters of Individual Solar Cells in a c-Si Module Extracted Using Indoor and Outdoor Electroluminescence Imaging

In this article, electroluminescence (EL) imaging is applied to characterize monocrystalline silicon photovoltaic (PV) modules under indoor and outdoor conditions. EL images of PV modules are analyzed to extract dark saturation current density and series resistance for individual cells within the module. These individual solar cells' parameters together with the terminal voltages of the modules, are used to model the current-voltage (I-V) characteristics of individual cells and the module using the one-diode model of solar cells and LTspice. We have explored the method for both indoor and outdoor EL images and compared the results with indoor I-V measurements under standard test conditions (STC). The electrical parameters of the PV module estimated using this approach for both, indoor and outdoor EL measurements are in good agreement with a relatively small deviation of less than 3% from the reference measurements performed under STC. This method is particularly useful to extract the electrical parameter and information on module degradation in the field using the EL only, without performing an I-V measurement.

The advancement of blood cell research by optical tweezers

Abstract Demonstration of the light radiation pressure on a microscopic level by A. Ashkin led to the invention of optical tweezers (OT). Applied in the studies of living systems, OT have become a preferable instrument for the noninvasive study of microobjects, allowing manipulation and measurement of the mechanical properties of molecules, organelles, and cells. In the present paper, we overview OT applications in hemorheological research, placing emphasis on red blood cells but also discussing OT applications for the investigation of the biomechanics of leukocytes and platelets. Blood properties have always served as a primary parameter in medical diagnostics due to the interconnection with the physiological state of an organism. Despite blood testing being a well-established procedure of conventional medicine, there are still many complex processes that must be unraveled to improve our understanding and contribute to future medicine. OT are advancing single-cell research, promising new insights into individual cell characteristics compared to the traditional approaches. We review the fundamental and practical findings revealed in blood research through the optical manipulation, stretching, guiding, immobilization, and inter-/intracellular force measurements of single blood cells.

Towards non-destructive individual cell I-V characteristic curve extraction from photovoltaic module measurements

In this work, a non-destructive, automated procedure to extract the I-V characteristics of individual cells of fully encapsulated photovoltaic (PV) modules is proposed. The approach is able to correctly identify and extract the electrical parameters of underperforming cells, due for example to defects or degradation. The approach uses multiple I-V measurements on the PV module assuming specific levels of shading on the individual cells. The single circuit models for the cells are obtained through the solution of an inverse fitting problem. The approach was validated in a simulated environment through statistical analysis, with the cell parameters based on real silicon PV devices. The computational complexity of the approach is also investigated and validation examples with different configurations of PV modules, including bypass diodes are presented. The approach was validated on several tests to assess noise robustness, flexibility in terms of cells non-uniformities and scalability towards larger systems, resulting in a lean and accurate procedure for I-V curves extraction. The proposed methodology can be potentially utilised for automated quality assurance and fault assessment of PV modules, investigation of degradation mechanisms of cells in PV modules, or quantitative validation of other optical imaging techniques such as luminescence imaging and infrared thermography.

Direct monitoring of drug-induced mechanical response of individual cells by atomic force microscopy.

Mechanical characteristics of individual cells play a vital role in many biological processes and are considered as indicators of the cells' states. Disturbances including methyl-β-cyclodextrin (MβCD) and cytochalasin D (cytoD) are known to significantly affect the state of cells, but little is known about the real-time response of single cells to these drugs in their physiological condition. Here, nanoindentation-based atomic force microscopy (AFM) was used to measure the elasticity of human embryonic kidney cells in the presence and absence of these pharmaceuticals. The results showed that depletion of cholesterol in the plasma membrane with MβCD resulted in cell stiffening whereas depolymerization of the actin cytoskeleton by cytoD resulted in cell softening. Using AFM for real-time measurements, we observed that cells mechanically responded right after these drugs were added. In more detail, the cell´s elasticity suddenly increased with increasing instability upon cholesterol extraction while it is rapidly decreased without changing cellular stability upon depolymerizing actin cytoskeleton. These results demonstrated that actin cytoskeleton and cholesterol contributed differently to the cell mechanical characteristics.

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition.

SummaryIn situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.

Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

In this paper, we present a novel optical microfluidic cytometry scheme for label-free detection of cells that is based on the self-mixing interferometry (SMI) technique. This device enables simple, fast and accurate detection of the individual cell characteristics and efficient cell type classification. We also propose a novel parameter to classify the cell or particle size. Artificial polystyrene beads and human living cells were measured using this system, and the SMI signal properties were statistically evaluated. The capability of the proposed cytometer for cell type discrimination and size classification has been validated by the measurement results. Our study can provide a very simple technique for cell enumeration and classification without any extra devices such as high-speed camera, photomultiplier and spectrometer. Moreover, the fluorescence staining operation which is necessary in traditional flow cytometry methods is not required either in our system.

Cell swelling, softening and invasion in a three-dimensional breast cancer model.

Sculpting of structure and function of three-dimensional multicellular tissues depend critically on the spatial and temporal coordination of cellular physical properties, yet the organizational principles that govern these events, and their disruption in disease, remain poorly understood. Using a multicellular mammary cancer organoid model, here we map in three dimensions the spatial and temporal evolution of positions, motions, and physical characteristics of individual cells. Compared with cells in the organoid core, cells at the organoid periphery and the invasive front are found to be systematically softer, larger and more dynamic. These mechanical changes are shown to arise from supracellular fluid flow through gap junctions, suppression of which delays transition to an invasive phenotype. Together, these findings highlight the role of spatiotemporal coordination of cellular physical properties in tissue organization and disease progression.

The Applications of Lattice Light-sheet Microscopy for Functional Volumetric Imaging of Hippocampal Neurons in a Three-Dimensional Culture System.

The characterization of individual cells in three-dimensions (3D) with very high spatiotemporal resolution is crucial for the development of organs-on-chips, in which 3D cell cultures are integrated with microfluidic systems. In this study, we report the applications of lattice light-sheet microscopy (LLSM) for monitoring neuronal activity in three-dimensional cell culture. We first established a 3D environment for culturing primary hippocampal neurons by applying a scaffold-based 3D tissue engineering technique. Fully differentiated and mature hippocampal neurons were observed in our system. With LLSM, we were able to monitor the behavior of individual cells in a 3D cell culture, which was very difficult under a conventional microscope due to strong light scattering from thick samples. We demonstrated that our system could study the membrane voltage and intracellular calcium dynamics at subcellular resolution in 3D under both chemical and electrical stimulation. From the volumetric images, it was found that the voltage indicators mainly resided in the cytosol instead of the membrane, which cannot be distinguished using conventional microscopy. Neuronal volumetric images were sheet scanned along the axial direction and recorded at a laser exposure of 6 ms, which covered an area up to 4800 μm2, with an image pixel size of 0.102 μm. When we analyzed the time-lapse volumetric images, we could quantify the voltage responses in different neurites in 3D extensions.

Local acoustic field enhancement of single cell photoacoustic signal detection based on metamaterial structure

The photoacoustic signal from single cell plays an important role in medical diagnosis. Currently, the single-cell photoacoustic detection has limitations. To overcome the limits, an acoustic metamaterial model based on the acoustic waveguide theory for local enhancement of the ultrahigh frequency (UHF) acoustic signals was implemented in this study and it was applied to study the acoustic signal characteristics of individual cells. The local field enhancement effect at different frequencies and the specific mechanism of the model was evaluated in the simulation experiment. This proposing acoustic metamaterial structure of good practical value can be used in various applications for acoustic signal processing.

Multimodal single-cell approaches shed light on T cell heterogeneity.

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.

Time course images of cellular injury and recovery in murine brain with high-resolution GRIN lens system

Time course, in vivo imaging of brain cells is crucial to fully understand the progression of secondary cellular damage and recovery in murine models of injury. We have combined high-resolution gradient index lens technology with a model of diffuse axonal injury in rodents to enable repeated visualization of fine features of individual cells in three-dimensional space over several weeks. For example, we recorded changes in morphology in the same axons in the external capsule numerous times over 30 to 60 days, before and after induced traumatic brain injury. We observed the expansion of secondary injury and limited recovery of individual axons in this subcortical white matter tract over time. In another application, changes in microglial activation state were visualized in the penumbra region of mice before and after ischemia induced by middle carotid artery occlusion. The ability to collect a series of high-resolution images of cellular features of the same cells pre- and post-injury enables a unique opportunity to study the progression of damage, spontaneous healing, and effects of therapeutics in mouse models of neurodegenerative disease and brain injury.

Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumors.

Single-cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single-cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor-infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single-cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.

Исследование адгезии эндотелиоцитоподобных клеток EA.hy926 к различным подложкам, используемых в методе мягкой литографии для создания микрофлюидных чипов

Адгезия клеток к субстрату является одним из ключевых факторов, влияющих на клеточные функции и их жизнеспособность. Для адекватного биомоделирования многоклеточных тканевых структур in vitro необходим тщательный подбор используемых в качестве подложки для клеток материалов. В работе мы исследовали адгезию эндотелиоцитоподобных клеток EA.hy926 к стеклу и полимерным материалам, часто применяемых в микрофлюидике для моделирования тканевых структур. Методом конфокальной сканирующей микроскопии было показано, что различные виды полидиметилсилоксана (ПДМС) характеризуется низкой адгезией клеток по сравнению со стеклом и специально подготовленным полистиролом (ПС). При этом обработка кислородной плазмой не оказывает существенного влияния на адгезию. Обработка коллагеном I, напротив, существенно улучшает адгезию. Белковый гель достаточно хорошо покрывает как гидрофильную поверхность стекла, так и гидрофобную поверхность полимера. Методом сканирующей зондовой микроскопии были также исследованы особенности распластывания отдельных клеток на поверхности субстрата. Cell-substrate adhesion is a key factor affecting the cell functionality and fate. Therefore, for adequate biomodeling of cellular structures in vitro, it is necessary to carefully select the materials used. In this study, we evaluated adhesion of endothelial-like cells EA.hy926 to polymeric materials and glass, which are used in microfluidics to model blood vessels. It was shown that different types of polydimethylsiloxane (PDMS) provided low cell adhesion compared to glass and specially prepared polystyrene (PS). The treatment with oxygen plasma did not exert a significant effect on adhesion. Covering with collagen I, on the contrary, significantly improved adhesion. The protein gel quite well covered both the hydrophilic surface of the glass and the hydrophobic surface of the polymer. The features of individual cells spreading on the substrate surface were also studied using scanning probe microscopy.