Position Of Individual Cells Research Articles

Extended methods for spatial cell classification with DBSCAN-CellX

Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.

InsideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology.

A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells' spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences.

Abstract LB558: Single cell profiling of PBMCs reveals correlates of clinical response to glofitamab

Abstract Glofitamab is a novel CD20-targeted bispecific T cell-engaging antibody that has demonstrated significant clinical efficacy in aggressive forms of Non-Hodgkin Lymphoma (aNHL). Despite significant clinical efficacy in aNHL (Hutchings et al. JCO 2021), some patients only achieve a partial response or progress upon treatment. To investigate the cellular and molecular factors that correlate with clinical activity of glofitamab, we applied CyTOF, single cell RNA and TCR sequencing to study PBMCs collected at baseline and at cycle 2 of treatment from patients with aNHL. Our cohort included 9 patients with complete response (CR) and 9 patients with progressive disease (PD) status (response assessed at cycle 3) recruited during dose escalation of glofitamab in a Ph1 clinical trial (NP30179). We identified a higher proportion of CD4 T cells and a lower proportion of monocytes in CR versus PD patients. Complementary to changes in cell proportions, we explored the covariation of genes across single cells to isolate co-regulated processes and identified several gene expression programs associated with the response status, of which some were shared between two or more cell types while others were cell type-specific. Focusing on the T cell compartment, we further identified subtypes of CD8 and CD4 T cells enriched in either CR or PD patients, and used topic modeling, a computational approach that excels in settings of continuous phenotypes, to contrast the transcriptional state of T cells in CR patients compared to PD patients at baseline and on treatment. Interestingly, we found CR patients to exhibit a less exhausted CD8 T cell population at baseline, confirming our previous observations using tumor bulk RNA sequencing data. Moreover, we observed that hyper-expanded clonotypes were generally enriched in the CD8 T cell compartment, and that the clonal cells had a stronger cytotoxic phenotype compared to non-clonal T cells. This observation was further corroborated when looking at TCR diversity. Indeed, we observed that distinct T cell populations exhibited differences in TCR diversity, with exhausted CD8 T cells showing decreased clonal diversity. Finally, to better understand the changes in CD8 T cells, we performed trajectory inference and observed that the position of individual cells along the pseudotime varied largely according to timepoint and response status. This analysis further allowed us to identify the expression dynamic of numerous markers of T cell exhaustion, as well as transcription factors associated with progenitor and self-renewing state. Notwithstanding the limited sample size and patient heterogeneity, our analysis provides a deep characterization of the cellular and molecular features associated with response to glofitamab using peripheral biomarkers. As glofitamab is being evaluated in a number of clinical trials either as a single agent or in combination, our dataset and findings can help broaden the understanding of its mode of action, and might be relevant for patient enrichment and monitoring response. Citation Format: Sina Nassiri, Lucas Habegger, Petra Gerber, Vinko Tosevski, Tamara Hüsser, Emilio Yanguez, Sylvia Herter, Martin Weisser, Koorosh Korfi, Pablo Umana, Emily Piccione, Ann-Marie Bröske, Marina Bacac. Single cell profiling of PBMCs reveals correlates of clinical response to glofitamab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB558.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Voronoi patterns in liquid crystal textures

An inverse Voronoi tessellation algorithm is developed that can be applied to a large variety of liquid crystalline textures, yielding the seed positions of individual cells. The patterns formed via Voronoi tessellation describe those observed in the texture micrographs very well, with a similarity in excess of 80%, when employing a harsh numerical measure to describe the similarity between experimental and calculated patterns. This is shown for a number of very different liquid crystalline systems, such as nematics on polymer surfaces, chiral materials like frustrated Blue Phases and cholesteric defect structures, discotics, bolaamphiphiles with columnar phases and metallomesogens. Discrepancies between calculated Voronoi patterns and experimental textures are mainly due to inevitable temperature gradients and nucleation fluctuations, which can in principle not be accounted for by the Voronoi algorithm. The methodology can be used for example in materials optimisation, the construction of complex photonic or diffraction devices and even for electronics with molecular wires based on columnar phases.

Tunnel dielectrophoresis for ultra-high precision size-based cell separation.

In molecular and cellular biological research, cell isolation and sorting are required for accurate investigation of cell populations of specific physical or biological characteristics. By employing unique cell properties to distinguish between heterogeneous cell populations, rapid and accurate sorting with high efficiency is possible. Dielectrophoresis-based cell manipulation has significant promise for separation of cells based on their physical properties and is used in diverse areas ranging from cellular diagnostics to therapeutic applications. In this study, we present a microfluidic device that can achieve label-free and size-based cell separation with high size differential resolution from a mono-cellular population or complex sample matrices. It was realized by using the tunnel dielectrophoresis (TDEP) technique to manipulate the spatial position of individual cells three dimensionally with high resolution. Cells were processed in high speed flows in high ionic strength buffers. A mixture of different sizes of polystyrene micro-particles with a size difference as small as 1 μm can be separated with high purity (>90%). For the first time, high-pass, low-pass, and band-pass filtering within a mono-cellular mammalian cell population were demonstrated with a tunable bandwidth as small as 3 μm. In addition, leukocyte subtype separation was demonstrated by sorting monocytes out of peripheral blood mononuclear cells (PBMCs) from whole blood with high purity (>85%). Its ability to deliver real-time adjustable cut-off threshold size-based cell sorting and its capability to provide an arbitrary cell size pick-up band could potentially enable many research and clinical applications.

Prediction of cell position using single-cell transcriptomic data: an iterative procedure.

Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.

Single-cell patterning technology for biological applications.

Single-cell patterning technology has revealed significant contributions of single cells to conduct basic and applied biological studies in vitro such as the understanding of basic cell functions, neuronal network formation, and drug screening. Unlike traditional population-based cell patterning approaches, single-cell patterning is an effective technology of fully understanding cell heterogeneity by precisely controlling the positions of individual cells. Therefore, much attention is currently being paid to this technology, leading to the development of various micro-nanofabrication methodologies that have been applied to locate cells at the single-cell level. In recent years, various methods have been continuously improved and innovated on the basis of existing ones, overcoming the deficiencies and promoting the progress in biomedicine. In particular, microfluidics with the advantages of high throughput, small sample volume, and the ability to combine with other technologies has a wide range of applications in single-cell analysis. Here, we present an overview of the recent advances in single-cell patterning technology, with a special focus on current physical and physicochemical methods including stencil patterning, trap- and droplet-based microfluidics, and chemical modification on surfaces via photolithography, microcontact printing, and scanning probe lithography. Meanwhile, the methods applied to biological studies and the development trends of single-cell patterning technology in biological applications are also described.

Prediction of cell position using single-cell transcriptomic data: an iterative procedure

Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. To develop new algorithms for this purpose, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). In the spirit of this framework, we describe here the proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.

Real-Time Determination of the Cell-Cycle Position of Individual Cells within Live Tumors Using FUCCI Cell-Cycle Imaging.

Most cytotoxic agents have limited efficacy for solid cancers. Cell-cycle phase analysis at the single-cell level in solid tumors has shown that the majority of cancer cells in tumors is not cycling and is therefore resistant to cytotoxic chemotherapy. Intravital cell-cycle imaging within tumors demonstrated the cell-cycle position and distribution of cancer cells within a tumor, and cell-cycle dynamics during chemotherapy. Understanding cell-cycle dynamics within tumors should provide important insights into novel treatment strategies.

New insights in chemokine signaling.

Chemokine signaling is essential for coordinated cell migration in health and disease to specifically govern cell positioning in space and time. Typically, chemokines signal through heptahelical, G protein-coupled receptors to orchestrate cell migration. Notably, chemokine receptors are highly dynamic structures and signaling efficiency largely depends on the discrete contact with the ligand. Promiscuity of both chemokines and chemokine receptors, combined with biased signaling and allosteric modulation of receptor activation, guarantees a tightly controlled recruitment and positioning of individual cells within the local environment at a given time. Here, we discuss recent insights in understanding chemokine gradient formation by atypical chemokine receptors and how typical chemokine receptors can transmit distinct signals to translate guidance cues into coordinated cell locomotion in space and time.

Transcriptional dynamics, denucleation, and gene regulation in embryonic lens development

SummaryTranscription of genes occurs in apparently irregular pulses of activity termed transcriptional bursts. In contrast, cellular differentiation is marked by coordinated gene expression of batteries of genes required for cellular specialization. Ocular lens is comprised of two compartments: the lens epithelium and lens fibers. Position of individual cells within the lens is directly related to their differentiation status, marked by expression of individual crystallin genes. Another hallmark of lens fiber cell differentiation is organized degradation of their organelles. It is currently not known how transcription is terminated in the disintegrating lens fiber cell nuclei and what are the external and internal components of this process critical for lens transparency. Onset of expression of different crystallins follows the temporal pattern Cryaa > Crybb1 > Cryga in differentiating lens fibers. Surprisingly, expression of Cryaa is terminated in “normal” fiber cell nuclei while expression of Cryga persists even in highly condensed nuclei just about to be disintegrated. Differentiation influences both transcriptional burst size and frequency as well as spatial pairing of different chromosomes that carry crystallin genes.

Selective position of individual cells without lysis on a circular window array using dielectrophoresis in a microfluidic device

We trapped individual cells between two circular windows using negative dielectrophoretic (DEP) force and then sequentially trapped them inside circular windows by positive DEP force without electrical lysis in a microfluidic device. Three parameters, (1) the transmembrane potential that determines the lysis of a cell, (2) individual cell size that affects the trapped position accuracy of the cell, and (3) the Clausius–Mossotti (CM) factor that decides the trapped efficiency of the cell, were characterized experimentally and numerically in this sequential cell trapping technique. In this characterization, we confirmed that the swap rate of applied voltage frequency, size similarity between the cell and circular window, and instantaneous change rate of Re(f CM) as a function of frequency were important factors in determining the selective position of individual cells without lysis. Our results provide useful suggestions for designing the structure of microfluidic DEP devices and optimizing variables required to manipulate individual cell trapping using both negative and positive DEP forces.

SU‐G‐TeP3‐07: On the Development of Mechano‐Biological Assessment of Leukemia Cells Using Optical Tweezers

Purpose:Patients with BCR‐ABL (Ph +ve) acute lymphoblastic leukemia are at very high risk of relapse and mortality. In line with the NIH mission to understand the physical and biological processes, we seek to report mechano‐biological method to assessment and distinguish treated/untreated leukemia cells.Methods:BCR‐ABL leukemia cell populations and silica microspheres were trapped in a 100x magnification optical trapping system (λ=660 nm, 70 mW). Light refracted through the trapped sample was collected in the back focal plane by a quadrant detector to measure the positions of individual cells. The sample was driven at a known frequency and amplitude with a flexure translation stage, and the target's response was recorded. The measured response was calibrated using the known driving parameters, and information about cell movements due to mechano‐biological effects was extracted. Two leukemia cell populations were tested: a control group and a group treated with 2 Gy.Results:The mechano‐biological movements of 10 microspheres, control cells, and treated cells were tracked over a ∼30 minute window at 1 minute intervals. The microsphere population did not see significant change in mechano‐biological movements over the testing interval and remained constant. The control cell population saw a two‐fold rise in activity that peaked around 1200 seconds, then dropped off sharply. The treated cell population saw a two‐fold rise in activity that peaked at 400 seconds, and dropped off slowly.Conclusion:The investigated technique allows for direct measurement the movements of a trapped object due to mechano‐biological effects such as thermal and extracellular motion. When testing microspheres, the mechano‐biological activity remained constant over time due to the lack of biological factors. In both the control and treated cell populations, the mechano‐biological activity was increased, possibly due to mitochondrial activation. This extra activity decreased over time, possibly due to cellular damage from trapping radiation.

A conserved rule for pancreatic islet organization.

Morphogenesis, spontaneous formation of organism structure, is essential for life. In the pancreas, endocrine , , and cells are clustered to form islets of Langerhans, the critical micro-organ for glucose homeostasis. The spatial organization of endocrine cells in islets looks different between species. Based on the three-dimensional positions of individual cells in islets, we computationally inferred the relative attractions between cell types, and found that the attractions between homotypic cells were slightly, but significantly, stronger than the attractions between heterotypic cells commonly in mouse, pig, and human islets. The difference between cell attraction and cell attraction was minimal in human islets, maximizing the plasticity of islet structures. Our result suggests that although the cellular composition and attractions of pancreatic endocrine cells are quantitatively different between species, the physical mechanism of islet morphogenesis may be evolutionarily conserved.

Being Squeezed into the Right Place within the Egg Shell

The development of an organism is a fascinating showdown of biology’s might, where just a single fertilized cell gives rise to all cells that together build the multifaceted parts of an organism. An elaborate biological machinery of proteins and metabolites works together to specify individual cell positions and cell identities. How could the simple rules of physics assist the dynamics of development? The physical forces that minimize surface tension have proven to be very successful in determining cell position within tissues (1). Cell shape and arrangement (2) and cell sorting (3) result from the balance of surface forces generated from cell properties such as cell contractility and cell-cell adhesion. In this issue, Struntz et al. (4) show that the forces arising from elastic repulsion between cells account for the cell positions during the first cycles of embryogenesis of the nematode (roundworm), Caenorhabditis elegans. The development of nematodes is characterized by cells acquiring their unique identity at an early stage. In fact, the entire cell lineage is invariant between different eggs and fully documented for C. elegans (5). Cells acquire their different identities by the unequal distribution of some factors during asymmetric cell division. In early embryogenesis, cell identity is also specified by a cell’s interaction with its immediate neighboring cells. Therefore, the positions of cells within the egg shell are important in defining cell identity and ensuring successful development. How does the embryo achieve a robust and reliable positioning of cells? Struntz et al. (4) use gentle yet fast, three-dimensional fluorescence microscopy by employing selective plane illumination microscopy to trace the trajectories of individual cell nuclei during early embryogenesis. Variation of the nuclei positions between individual eggs is observed to be <2 μm within a 50-μm-long egg. Cell nucleus position is therefore found to be a very reliable and robust readout of cell positioning. Observed nuclei trajectories are fully accounted for within a physical model describing three-dimensional nuclei trajectories as stochastic relaxation dynamics driven by repulsive forces between neighboring cells and the cells and the egg shell. In the picture of the model, the cells act like soft spheres with the center of the nucleus at the center of the sphere. Being squeezed together into an egg shell, the deformation of theses soft spheres causes an elastic repulsion that forces the nuclei and thus the cells to relax into their positions (see Fig. 1). With every cell division, one big sphere is divided into two smaller ones and all spheres rearrange to make most use of the limited space provided within the egg shell. The sphere’s centers, i.e., the nuclei, diffuse to positions where repulsive forces are minimal. The model employs measured cell division times, orientation of the division axes, and cell division asymmetry, and allows the observation of deviations from normal trajectories when these input parameters are modified in the simulation. Figure 1 Two-dimensional sketch illustrating the repulsive forces rearranging cell nuclei (orange) after the cell divisions that gave rise to the four-cell stage. (Solid circles) Position of nuclei shortly after division; (dashed circles) position after relaxation. ... The authors find that the nuclei positions are robust regarding the exact timing of cell divisions as long as the sequence of cell divisions is kept in order. In contrast, changes in the orientation of cell division axes are found to result in very different nuclei trajectories. The orientation of cell division axes therefore needs tight control. In C. elegans’ first cell division, mechanical forces have been identified to control the orientation of the cell division axis and the asymmetry of the division (6). It would be fascinating to find out how physical forces and the resulting cell shapes are correlated with division axes’ orientation at later stages. At large numbers of cells (>12), the repulsive forces alone are found to be insufficient to reliably determine cell nuclei position. At this stage, the packing problem becomes mathematically difficult and may have multiple equally good solutions, so guidance by biological signaling is needed for direction. The work by Struntz et al. (4) provides a fascinating physical framework that can, in future, be complemented with the observation and perturbation of biological factors to study how biochemical signaling and mechanical forces are intertwined to coordinate development. During the last decade, surface forces have been found to assist in multiple processes during the development of organisms (7). Struntz et al. (4) now show that the elastic forces of mutual repulsion between cells are enough to explain the robust positioning of cells during the early stages of C. elegans embryogenesis. This is an elegant example of simplicity in the midst of the bewildering complexity of development.

A pattern recognition method for lattice distortion measurement from HRTEM images

The idea of the method is to analyse a crystal lattice by creating a grid of quadrilaterals corresponding to repeated cells that are visible in the image. This approach combines image processing elements with a continuum field theory, to create a distortion-independent similarity measure that is used to select the most appropriate among possible lattice configurations. Subsequently, displacement and distortion fields are computed from individual cell positions. The method allows one to obtain these fields even for images where a periodic cell does not necessarily appear as a single dot of intensity in a high-resolution transmission electron microscopy (HRTEM) image, which results in a lower accuracy of commonly used approaches, namely geometric phase and peak finding. The results obtained from this method are verified quantitatively by comparison with known distortion tensor distributions and Burgers vector values on both simulated and real images.

Unstained viable cell recognition in phase-contrast microscopy

AbstractIndividual cell recognition is a relevant task to be accomplished when single-ion microbeam irradiations are performed. At INFN-LNL facility cell visualization system is based on a phase-contrast optical microscope, without the use of any cell dye. Unstained cells are seeded in the special designed Petri dish, between two mylar foils, and at present the cell recognition is achieved manually by an expert operator. Nevertheless, this procedure is time consuming and sometimes it could be not practical if the amount of living cells to be irradiated is large. To reduce the time needed to recognize unstained cells on the Petri dish, it has been designed and implemented an automated, parallel algorithm. Overlapping ROIs sliding in steps over the captured grayscale image are firstly pre-classified and potential cell markers for the segmentation are obtained. Segmented objects are additionally classified to categorize cell bodies from other structures considered as sample dirt or background. As a result, cell coordinates are passed to the dedicated CELLView program that controls all the LNL single-ion microbeam irradiation protocol, including the positioning of individual cells in front of the ion beam. Unstained cell recognition system was successfully tested in experimental conditions with two different mylar surfaces. The recognition time and accuracy was acceptable, however, improvements in speed would be useful.

Multi-Position Photoactivation and Multi-Time Acquisition for Large-Scale Cell Tracing in Avian Embryos

Vertebrate development is a complex orchestration of cell and tissue movements. Tracing individual cell positions can rapidly become a large-scale problem because cell numbers often grow exponentially in the early embryo. A typical approach consists of fluorescently marking small numbers of cells within a large number of embryos, followed by comprehensive three-dimensional static or time-lapse imaging to map cell positions. However, for large-scale cell tracing, such as during organogenesis, the time, effort, and expense of this approach can be limiting. The multi-position acquisition method can be used to capture more than one location on a microscope stage and allow for multi-specimen imaging. When combined with photoactivation cell labeling, a tool for selective cell marking using laser excitation, multi-position imaging offers a powerful tool for rapid data acquisition. This protocol describes the technique and demonstrates its use to map cell movements in the chick spinal cord, using slice culture explants. The details of multiple slice culture preparation, multi-position photoactivation, and multi-time acquisition are described. These events are coordinated by setting up blocks of microscope instructions that execute sequentially. This method significantly decreases the time, effort, microscopy, and embryo costs by a factor of the number of specimens imaged per session, typically six.

An automatic procedure for evaluation of single cell motility

Cell motility is vital in many physiological and pathological processes, such as embryogenesis, inflammation, wound healing, and metastasis. However, the time-consuming step in the evaluation of individual cell motility is the analysis of hundreds of recorded images of cell cultures in general consisting of retrieving images, one at a time, and marking the positions of individual cells by a computer mouse. Therefore, the aim of the present study was to develop a novel automatic procedure for the evaluation of cell motility. The procedure was tested on fibroblasts and glioma and adenocarcinoma cells engineered to express the green fluorescent protein by either transient transfection or adenovirus transduction, allowing automatic recognition of cell coordinates on retrieved images. The effects of serum growth factors, teratogenic compounds, and overexpression of transcription factors on the motile behavior of cultured cells were determined. Cell motility was estimated by both manual and automatic marking of cell position and subsequently motility parameters were computed. The results obtained by the two procedures were found to correlate significantly. We developed a procedure allowing automatic video recording of sparsely seeded cells transfected with a plasmid or tranduced with a recombinant virus expressing the enhanced green fluorescent protein (EGFP).