Journal articles on the topic 'Automated cell to signal'
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Frank, Tino, and Savaş Tay. "Automated co-culture system for spatiotemporal analysis of cell-to-cell communication." Lab on a Chip 15, no. 10 (2015): 2192–200. http://dx.doi.org/10.1039/c5lc00182j.
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Bußmann, Agnes, Thomas Thalhofer, Sophie Hoffmann, Leopold Daum, Nivedha Surendran, Oliver Hayden, Jürgen Hubbuch, and Martin Richter. "Microfluidic Cell Transport with Piezoelectric Micro Diaphragm Pumps." Micromachines 12, no. 12 (November 27, 2021): 1459. http://dx.doi.org/10.3390/mi12121459.
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The automated transport of cells can enable far-reaching cell culture research. However, to date, such automated transport has been achieved with large pump systems that often come with long fluidic connections and a large power consumption. Improvement is possible with space- and energy-efficient piezoelectric micro diaphragm pumps, though a precondition for a successful use is to enable transport with little to no mechanical stress on the cell suspension. This study evaluates the impact of the microfluidic transport of cells with the piezoelectric micro diaphragm pump developed by our group. It includes the investigation of different actuation signals. Therewith, we aim to achieve optimal fluidic performance while maximizing the cell viability. The investigation of fluidic properties proves a similar performance with a hybrid actuation signal that is a rectangular waveform with sinusoidal flanks, compared to the fluidically optimal rectangular actuation. The comparison of the cell transport with three actuation signals, sinusoidal, rectangular, and hybrid actuation shows that the hybrid actuation causes less damage than the rectangular actuation. With a 5% reduction of the cell viability it causes similar strain to the transport with sinusoidal actuation. Piezoelectric micro diaphragm pumps with the fluidically efficient hybrid signal actuation are therefore an interesting option for integrable microfluidic workflows.
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KRISHNAN, M. MUTHU RAMA, S. VINITHA SREE, DHANJOO N. GHISTA, EDDIE Y. K. NG, SWAPNA, ALVIN P. C. ANG, KWAN-HOONG NG, and JASJIT S. SURI. "AUTOMATED DIAGNOSIS OF CARDIAC HEALTH USING RECURRENCE QUANTIFICATION ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240014. http://dx.doi.org/10.1142/s0219519412400143.
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The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.
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Francis, Michael, Xun Qian, Chimène Charbel, Jonathan Ledoux, J. C. Parker, and Mark S. Taylor. "Automated region of interest analysis of dynamic Ca2+ signals in image sequences." American Journal of Physiology-Cell Physiology 303, no. 3 (August 1, 2012): C236—C243. http://dx.doi.org/10.1152/ajpcell.00016.2012.
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Ca2+ signals are commonly measured using fluorescent Ca2+ indicators and microscopy techniques, but manual analysis of Ca2+ measurements is time consuming and subject to bias. Automated region of interest (ROI) detection algorithms have been employed for identification of Ca2+ signals in one-dimensional line scan images, but currently there is no process to integrate acquisition and analysis of ROIs within two-dimensional time lapse image sequences. Therefore we devised a novel algorithm for rapid ROI identification and measurement based on the analysis of best-fit ellipses assigned to signals within noise-filtered image sequences. This algorithm was implemented as a plugin for ImageJ software (National Institutes of Health, Bethesda, MD). We evaluated the ability of our algorithm to detect synthetic Gaussian signal pulses embedded in background noise. The algorithm placed ROIs very near to the center of a range of signal pulses, resulting in mean signal amplitude measurements of 99.06 ± 4.11% of true amplitude values. As a practical application, we evaluated both agonist-induced Ca2+ responses in cultured endothelial cell monolayers, and subtle basal endothelial Ca2+ dynamics in opened artery preparations. Our algorithm enabled comprehensive measurement of individual and localized cellular responses within cultured cell monolayers. It also accurately identified characteristic Ca2+ transients, or Ca2+ pulsars, within the endothelium of intact mouse mesenteric arteries and revealed the distribution of this basal Ca2+ signal modality to be non-Gaussian with respect to amplitude, duration, and spatial spread. We propose that large-scale statistical evaluations made possible by our algorithm will lead to a more efficient and complete characterization of physiologic Ca2+-dependent signaling.
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Wenus, Jakub, Heiko Düssmann, Perrine Paul, Dimitrios Kalamatianos, Markus Rehm, Peter E. Wellstead, Jochen H. M. Prehn, and Heinrich J. Huber. "ALISSA: an automated live-cell imaging system for signal transduction analyses." BioTechniques 47, no. 6 (December 2009): 1033–40. http://dx.doi.org/10.2144/000113247.
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Khandekar, Rohan, Prakhya Shastry, Smruthi Jaishankar, Oliver Faust, and Niranjana Sampathila. "Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis." Biomedical Signal Processing and Control 68 (July 2021): 102690. http://dx.doi.org/10.1016/j.bspc.2021.102690.
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Shen, Hailin, Glyn Nelson, David E. Nelson, Stephnie Kennedy, David G. Spiller, Tony Griffiths, Norman Paton, Stephen G. Oliver, Michael R. H. White, and Douglas B. Kell. "Automated tracking of gene expression in individual cells and cell compartments." Journal of The Royal Society Interface 3, no. 11 (June 14, 2006): 787–94. http://dx.doi.org/10.1098/rsif.2006.0137.
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Many intracellular signal transduction processes involve the reversible translocation from the cytoplasm to the nucleus of transcription factors. The advent of fluorescently tagged protein derivatives has revolutionized cell biology, such that it is now possible to follow the location of such protein molecules in individual cells in real time. However, the quantitative analysis of the location of such proteins in microscopic images is very time consuming. We describe CellTracker, a software tool designed for the automated measurement of the cellular location and intensity of fluorescently tagged proteins. CellTracker runs in the MS Windows environment, is freely available (at http://www.dbkgroup.org/celltracker/ ), and combines automated cell tracking methods with powerful image-processing algorithms that are optimized for these applications. When tested in an application involving the nuclear transcription factor NF-κB, CellTracker is competitive in accuracy with the manual human analysis of such images but is more than 20 times faster, even on a small task where human fatigue is not an issue. This will lead to substantial benefits for time-lapse-based high-content screening.
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Skinner, Benjamin Matthew, Joanne Bacon, Claudia Cattoni Rathje, Erica Lee Larson, Emily Emiko Konishi Kopania, Jeffrey Martin Good, Nabeel Ahmed Affara, and Peter James Ivor Ellis. "Automated Nuclear Cartography Reveals Conserved Sperm Chromosome Territory Localization across 2 Million Years of Mouse Evolution." Genes 10, no. 2 (February 1, 2019): 109. http://dx.doi.org/10.3390/genes10020109.
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Measurements of nuclear organization in asymmetric nuclei in 2D images have traditionally been manual. This is exemplified by attempts to measure chromosome position in sperm samples, typically by dividing the nucleus into zones, and manually scoring which zone a fluorescence in-situ hybridisation (FISH) signal lies in. This is time consuming, limiting the number of nuclei that can be analyzed, and prone to subjectivity. We have developed a new approach for automated mapping of FISH signals in asymmetric nuclei, integrated into an existing image analysis tool for nuclear morphology. Automatic landmark detection defines equivalent structural regions in each nucleus, then dynamic warping of the FISH images to a common shape allows us to generate a composite of the signal within the entire cell population. Using this approach, we mapped the positions of the sex chromosomes and two autosomes in three mouse lineages (Mus musculus domesticus, Mus musculus musculus and Mus spretus). We found that in all three, chromosomes 11 and 19 tend to interact with each other, but are shielded from interactions with the sex chromosomes. This organization is conserved across 2 million years of mouse evolution.
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Li, Stephen K. H., Nick Zabinyakov, Alexandre Bouzekri, Rita Straus, Raymond Jong, Michael Sullivan, Alexander Loboda, Daniel Majonis, and Christina Loh. "An automated approach to high-plex cytometric immunophenotyping with CyTOF XT." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 172.17. http://dx.doi.org/10.4049/jimmunol.208.supp.172.17.
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Abstract CyTOF® mass cytometry is a single-cell analysis platform that uses isotope-tagged antibodies to resolve 50-plus markers in a single tube without signal compensation, making CyTOF ideal for routine immunophenotyping. CyTOF XT™, the latest CyTOF system, features automated sample acquisition. Stained samples were acquired in parallel using the automated CyTOF XT system and manually, using the Helios™ system, to assess performance of the automated system. Multiple suspension mass cytometry staining workflows were evaluated. Population frequencies and resolution indices for markers were assessed by manual gating. There was no significant difference between population frequencies analyzed between the two CyTOF systems. On average, samples acquired on CyTOF XT resulted in greater resolution between positive and negative populations compared to Helios. The Maxpar® Direct™ Immune Profiling System, which comprises the Maxpar® Direct™ Immune Profiling Assay™ and Maxpar Pathsetter™ software, was also compared on the CyTOF XT and Helios systems. The Maxpar Direct Immune Profiling Assay includes a 30-marker panel in a dry, single-tube format for staining human whole blood or PBMC. Maxpar Pathsetter automates reporting of population statistics and stain assessments for the panel. Maxpar Pathsetter showed comparable population frequencies between the two CyTOF systems and improved staining assessment on CyTOF XT. Overall, these studies find that the CyTOF XT system generates better signal resolution than the Helios system. Automated acquisition by CyTOF XT enables researchers to accurately and reproducibly streamline human immunophenotyping. For Research Use Only. Not for use in diagnostic procedures.
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Meyer, Michael G., Mark Fauver, J. Richard Rahn, Thomas Neumann, Florence W. Patten, Eric J. Seibel, and Alan C. Nelson. "Automated cell analysis in 2D and 3D: A comparative study." Pattern Recognition 42, no. 1 (January 2009): 141–46. http://dx.doi.org/10.1016/j.patcog.2008.06.018.
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Nowak, Jacqueline, Kristin Gennermann, Staffan Persson, and Zoran Nikoloski. "CytoSeg 2.0: automated extraction of actin filaments." Bioinformatics 36, no. 9 (January 23, 2020): 2950–51. http://dx.doi.org/10.1093/bioinformatics/btaa035.
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Abstract Motivation Actin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task. Results We developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community. Availability and implementation CytoSeg 2.0 is open-source software under the GPL and is available on Github: https://github.com/jnowak90/CytoSeg2.0. Supplementary information Supplementary data are available at Bioinformatics online.
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Garner, Kathryn L. "High content imaging for monitoring signalling dynamics in single cells." Journal of Molecular Endocrinology 65, no. 4 (November 2020): R91—R100. http://dx.doi.org/10.1530/jme-20-0169.
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All living cells are sensors of their environment: they sense signals, hormones, cytokines, and growth factors, among others. Binding of these signals to cell surface receptors initiates the transmission of messages along intracellular signalling pathways through protein–protein interactions, enzymatic modifications and conformational changes. Typically, the activation of signalling pathways are monitored in whole populations of cells, giving population average measures, often using experimental methods that destroy and homogenise the cell population. High content imaging is an automated, high-throughput fluorescence microscopy method that enables measurements of signal transduction pathways to be taken from live cells. It can be used to measure signalling dynamics, how the abundance of particular proteins of interest change over time, or to record how particular proteins move and change their localisation in response to a signal from their environment. Using this, and other single cell methods, it is becoming increasingly clear that cells are in fact very variable in their response to a given stimulus and in the quantities of cellular components they express, even in clonal (isogenic) cell lines. This review will discuss how high content imaging has contributed to our growing understanding of cellular heterogeneity. It will discuss how data generated has been combined with information theoretic approaches to quantify the amount of information transferred through noisy signalling pathways. Lastly, the relevance of heterogeneity to our understanding and treatment of disease will be considered, highlighting the importance of single cell measurements.
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Martins, Joana R., Dominik Haenni, Milica Bugarski, Andreja Figurek, and Andrew M. Hall. "Quantitative intravital Ca2+ imaging maps single cell behavior to kidney tubular structure." American Journal of Physiology-Renal Physiology 319, no. 2 (August 1, 2020): F245—F255. http://dx.doi.org/10.1152/ajprenal.00052.2020.
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Ca2+ is an important second messenger that translates extracellular stimuli into intracellular responses. Although there has been significant progress in understanding Ca2+ dynamics in organs such as the brain, the nature of Ca2+ signals in the kidney is still poorly understood. Here, we show that by using a genetically expressed highly sensitive reporter (GCaMP6s), it is possible to perform imaging of Ca2+ signals at high resolution in the mouse kidney in vivo. Moreover, by applying machine learning-based automated analysis using a Ca2+-independent signal, quantitative data can be extracted in an unbiased manner. By projecting the resulting data onto the structure of the kidney, we show that different tubular segments display highly distinct spatiotemporal patterns of Ca2+ signals. Furthermore, we provide evidence that Ca2+ activity in the proximal tubule decreases with increasing distance from the glomerulus. Finally, we demonstrate that substantial changes in intracellular Ca2+ can be detected in proximal tubules in a cisplatin model of acute kidney injury, which can be linked to alterations in cell structure and transport function. In summary, we describe a powerful new tool to investigate how single cell behavior is integrated with whole organ structure and function and how it is altered in disease states relevant to humans.
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Venkatachalam, Vidya, Catherine Claude Martin, Chantal Jayat-Vignoles, Jean-Luc Faucher, Jean Feuillard, Phil Morrissey, and Thaddeus George. "Automated Classification of Normal and Leukemic B Cells on the ImageStream® Imaging Flow Cytometer Using Immunocytomorphological Image Characteristics." Blood 112, no. 11 (November 16, 2008): 4883. http://dx.doi.org/10.1182/blood.v112.11.4883.4883.
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Abstract Leukemic B cells can be distinguished from normal B cells based on their surface staining patterns and their morphology, but the differences can be subtle and the abnormal events rare. In order to reliably differentiate normal from leukemic B cells, objective quantitation of large data sets is required. This is accomplished with the ImageStream cytometer, which automatically acquires multispectral images of thousands of individual cells in flow at very high speeds. The associated IDEAS data analysis software measures hundreds of photometric and morphometric features per cell, thereby enabling simultaneous immunophenotyping and morphology-based measurements. The features quantify individual image characteristics such as shape, size, signal strength and texture. We have leveraged these features by implementing an automated classification routine that identifies the feature combination that best separates different cell types of interest. Our automated classification routine utilizes a modification of Fisher’s Linear Discriminant criterion to measure discrimination power between two or more populations of cells. The classifier generates a composite feature consisting of a weighted linear combination of basic image features that provides maximum discrimination. Thus, our method transforms a multi-parameter classification task that can be difficult to conceptualize into a classification based on a single composite feature that can be readily conceptualized and visualized using a simple histogram output. Since the composite feature is generated from, and tested and optimized on, large numbers of cells, we obtain results that are highly objective, repeatable, statistically significant and scalable with data size. We used this classification approach to study normal and tumor cells of the hematopoietic lineage with the goal of improving diagnosis of hematological disorders. Here we show the results of using automated classification to distinguish the subtle differences between B cells of CLL patients and those of normal patients. We also used the classifier to distinguish B-cell ALL from B-cell CLL. In both cases, we used a combination of shape, size, texture and signal strength features from the SSC, brightfield and nuclear imagery generated by the ImageStream system. The data presented demonstrate the application of a novel automated cell classification method to the discrimination of normal and tumor cells within large and heterogeneous cell samples.
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Cornelissen, Frans, Peter Verstraelen, Tobias Verbeke, Isabel Pintelon, Jean-Pierre Timmermans, Rony Nuydens, and Theo Meert. "Quantitation of Chronic and Acute Treatment Effects on Neuronal Network Activity Using Image and Signal Analysis." Journal of Biomolecular Screening 18, no. 7 (April 19, 2013): 807–19. http://dx.doi.org/10.1177/1087057113486518.
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Upon maturation, primary neuronal cultures form an interconnected network based on neurite outgrowth and synaptogenesis in which spontaneous electrical activity arises. Measurement of network activity allows quantification of neuronal health and maturation. A fluorescent indicator was used to monitor secondary calcium influxes after the occurrence of action potentials, allowing us to examine activity of hippocampal cultures via confocal live cell imaging. Subsequently, nuclear staining with DAPI allows accurate cell segmentation. To analyze the calcium recording in a robust, observer-independent manner, we implemented an automated image- and signal-processing algorithm and validated it against a visual, interactive procedure. Both methods yielded similar results on the emergence of synchronized activity and allowed robust quantitative measurement of acute and chronic modulation of drugs on network activity. Both the number of days in vitro (DIV) and neutralization of nerve growth factor (NGF) have a significant effect on synchronous burst frequency and correlation. Acute effects are demonstrated using 5-HT (serotonin) and ethylene glycol tetra-acetic acid. Automated analysis allowed measuring additional features, such as peak decay times and bursting frequency of individual neurons. Based on neuronal cell cultures in 96-well plates and accurate calcium recordings, the analysis method allows development of an integrated high-content screening assay. Because molecular biological techniques can be applied to assess the influence of genes on network activity, it is applicable for neurotoxicity or neurotrophics screening as well as development of in vitro disease models via, for example, pharmacologic manipulation or RNAi.
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Ferrante, Laura, Kashif Rajpoot, Mark Jeeves, and Christian Ludwig. "Automated analysis for multiplet identification from ultra-high resolution 2D-1H,13C-HSQC NMR spectra." Wellcome Open Research 7 (October 14, 2022): 262. http://dx.doi.org/10.12688/wellcomeopenres.18248.1.
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Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D-1H,13C-HSQC NMR spectra arising from 13C -13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D-1H,13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D-1H,13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.
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Lario, Antonio, Antonio González, and Gabriel Dorado. "Automated Laser-Induced Fluorescence DNA Sequencing: Equalizing Signal-to-Noise Ratios Significantly Enhances Overall Performance." Analytical Biochemistry 247, no. 1 (April 1997): 30–33. http://dx.doi.org/10.1006/abio.1996.9933.
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VAN DIJCK, GERT, KARSTEN SEIDL, OLIVER PAUL, PATRICK RUTHER, MARC M. VAN HULLE, and REINOUD MAEX. "ENHANCING THE YIELD OF HIGH-DENSITY ELECTRODE ARRAYS THROUGH AUTOMATED ELECTRODE SELECTION." International Journal of Neural Systems 22, no. 01 (February 2012): 1–19. http://dx.doi.org/10.1142/s0129065712003055.
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Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.
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Montanari, Mariele, Sabrina Burattini, Caterina Ciacci, Patrizia Ambrogini, Silvia Carloni, Walter Balduini, Daniele Lopez, Giovanna Panza, Stefano Papa, and Barbara Canonico. "Automated—Mechanical Procedure Compared to Gentle Enzymatic Tissue Dissociation in Cell Function Studies." Biomolecules 12, no. 5 (May 14, 2022): 701. http://dx.doi.org/10.3390/biom12050701.
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The first step to obtain a cellular suspension from tissues is the disaggregation procedure. The cell suspension method has to provide a representative sample of the different cellular subpopulations and to maximize the number of viable functional cells. Here, we analyzed specific cell functions in cell suspensions from several rat tissues obtained by two different methods, automated–mechanical and enzymatic disaggregation. Flow cytometric, confocal, and ultrastructural (TEM) analyses were applied to the spleen, testis, liver and other tissues. Samples were treated by an enzymatic trypsin solution or processed by the Medimachine II (MMII). The automated–mechanical and enzymatic disaggregation procedures have shown to work similarly in some tissues, which displayed comparable amounts of apoptotic/necrotic cells. However, cells obtained by the enzyme-free Medimachine II protocols show a better preservation lysosome and mitochondria labeling, whereas the enzymatic gentle dissociation appears to constantly induce a lower amount of intracellular ROS; nevertheless, lightly increased ROS can be recognized as a complimentary signal to promote cell survival. Therefore, MMII represents a simple, fast, and standardized method for tissue processing, which allows to minimize bias arising from the operator’s ability. Our study points out technical issues to be adopted for specific organs and tissues to obtain functional cells.
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Hernández-Herrera, Paul, Yamel Ugartechea-Chirino, Héctor H. Torres-Martínez, Alejandro V. Arzola, José Eduardo Chairez-Veloz, Berenice García-Ponce, María de la Paz Sánchez, et al. "Live Plant Cell Tracking: Fiji plugin to analyze cell proliferation dynamics and understand morphogenesis." Plant Physiology 188, no. 2 (November 15, 2021): 846–60. http://dx.doi.org/10.1093/plphys/kiab530.
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Abstract Arabidopsis (Arabidopsis thaliana) primary and lateral roots (LRs) are well suited for 3D and 4D microscopy, and their development provides an ideal system for studying morphogenesis and cell proliferation dynamics. With fast-advancing microscopy techniques used for live-imaging, whole tissue data are increasingly available, yet present the great challenge of analyzing complex interactions within cell populations. We developed a plugin “Live Plant Cell Tracking” (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells. The LiPlaCeT plugin contains ad hoc ergonomic curating tools, making it very simple to use for manual cell tracking, especially when the signal-to-noise ratio of images is low or variable in time or 3D space and when automated methods may fail. Performing time-lapse experiments and using cell-tracking data extracted with the assistance of LiPlaCeT, we accomplished deep analyses of cell proliferation and clonal relations in the whole developing LR primordia and constructed genealogical trees. We also used cell-tracking data for endodermis cells of the root apical meristem (RAM) and performed automated analyses of cell population dynamics using ParaView software (also publicly available). Using the RAM as an example, we also showed how LiPlaCeT can be used to generate information at the whole-tissue level regarding cell length, cell position, cell growth rate, cell displacement rate, and proliferation activity. The pipeline will be useful in live-imaging studies of roots and other plant organs to understand complex interactions within proliferating and growing cell populations. The plugin includes a step-by-step user manual and a dataset example that are available at https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip.
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Wollman, Adam J. M., and Mark C. Leake. "Millisecond single-molecule localization microscopy combined with convolution analysis and automated image segmentation to determine protein concentrations in complexly structured, functional cells, one cell at a time." Faraday Discussions 184 (2015): 401–24. http://dx.doi.org/10.1039/c5fd00077g.
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We present a single-molecule tool called the CoPro (concentration of proteins) method that uses millisecond imaging with convolution analysis, automated image segmentation and super-resolution localization microscopy to generate robust estimates for protein concentration in different compartments of single living cells, validated using realistic simulations of complex multiple compartment cell types. We demonstrate its utility experimentally on modelEscherichia colibacteria andSaccharomyces cerevisiaebudding yeast cells, and use it to address the biological question of how signals are transduced in cells. Cells in all domains of life dynamically sense their environment through signal transduction mechanisms, many involving gene regulation. The glucose sensing mechanism ofS. cerevisiaeis a model system for studying gene regulatory signal transduction. It uses the multi-copy expression inhibitor of the GAL gene family, Mig1, to repress unwanted genes in the presence of elevated extracellular glucose concentrations. We fluorescently labelled Mig1 molecules with green fluorescent protein (GFP)viachromosomal integration at physiological expression levels in livingS. cerevisiaecells, in addition to the RNA polymerase protein Nrd1 with the fluorescent protein reporter mCherry. Using CoPro we make quantitative estimates of Mig1 and Nrd1 protein concentrations in the cytoplasm and nucleus compartments on a cell-by-cell basis under physiological conditions. These estimates indicate a ∼4-fold shift towards higher values in the concentration of diffusive Mig1 in the nucleus if the external glucose concentration is raised, whereas equivalent levels in the cytoplasm shift to smaller values with a relative change an order of magnitude smaller. This compares with Nrd1 which is not involved directly in glucose sensing, and which is almost exclusively localized in the nucleus under high and low external glucose levels. CoPro facilitates time-resolved quantification of protein concentrations in single functional cells, and enables the distributions of concentrations across a cell population to be measured. This could be useful in investigating several cellular processes that are mediated by proteins, especially where changes in protein concentration in a single cell in response to changes in the extracellular chemical environment are subtle and rapid and may be smaller than the variability across a cell population.
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Basu, Sautami, Ravinder Agarwal, and Vishal Srivastava. "Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma." Biomedical Signal Processing and Control 76 (July 2022): 103728. http://dx.doi.org/10.1016/j.bspc.2022.103728.
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Pham, Huy, Emile R. Shehada, Shawna Stahlheber, Kushagra Pandey, and Wayne B. Hayes. "No Cell Left behind: Automated, Stochastic, Physics-Based Tracking of Every Cell in a Dense, Growing Colony." Algorithms 15, no. 2 (January 30, 2022): 51. http://dx.doi.org/10.3390/a15020051.
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Motivation: Precise tracking of individual cells—especially tracking the family lineage, for example in a developing embryo—has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation “model” of any scene that we observe. Because we understand simple truths about the world—for example cells can move and divide, but they cannot instantaneously move vast distances—this model “in our heads” helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. Results: Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby “Universes” and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presense of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.
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Zhao, Runchen, Siqi Cui, Zhuoxu Ge, Yuqi Zhang, Kaustav Bera, Lily Zhu, Sean X. Sun, and Konstantinos Konstantopoulos. "Hydraulic resistance induces cell phenotypic transition in confinement." Science Advances 7, no. 17 (April 2021): eabg4934. http://dx.doi.org/10.1126/sciadv.abg4934.
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Cells penetrating into confinement undergo mesenchymal-to-amoeboid transition. The topographical features of the microenvironment expose cells to different hydraulic resistance levels. How cells respond to hydraulic resistance is unknown. We show that the cell phenotype shifts from amoeboid to mesenchymal upon increasing resistance. By combining automated morphological tracking and wavelet analysis along with fluorescence recovery after photobleaching (FRAP), we found an oscillatory phenotypic transition that cycles from blebbing to short, medium, and long actin network formation, and back to blebbing. Elevated hydraulic resistance promotes focal adhesion maturation and long actin filaments, thereby reducing the period required for amoeboid-to-mesenchymal transition. The period becomes independent of resistance upon blocking the mechanosensor TRPM7. Mathematical modeling links intracellular calcium oscillations with actomyosin turnover and force generation and recapitulates experimental data. We identify hydraulic resistance as a critical physical cue controlling cell phenotype and present an approach for connecting fluorescent signal fluctuations to morphological oscillations.
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Chanloha, Pitipong, Jatuporn Chinrungrueng, Wipawee Usaha, and Chaodit Aswakul. "Traffic Signal Control with Cell Transmission Model Using Reinforcement Learning for Total Delay Minimisation." International Journal of Computers Communications & Control 10, no. 5 (July 19, 2015): 627. http://dx.doi.org/10.15837/ijccc.2015.5.2025.
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This paper proposes a new framework to control the traffic signal lights by<br />applying the automated goal-directed learning and decision making scheme, namely<br />the reinforcement learning (RL) method, to seek the best possible traffic signal ac-<br />tions upon changes of network state modelled by the signalised cell transmission model<br />(CTM). This paper employs the Q-learning which is one of the RL tools in order to<br />find the traffic signal solution because of its adaptability in finding the real time solu-<br />tion upon the change of states. The goal is for RL to minimise the total network delay.<br />Surprisingly, by using the total network delay as a reward function, the results were<br />not necessarily as good as initially expected. Rather, both simulation and mathemat-<br />ical derivation results confirm that using the newly proposed red light delay as the RL<br />reward function gives better performance than using the total network delay as the<br />reward function. The investigated scenarios include the situations where the summa-<br />tion of overall traffic demands exceeds the maximum flow capacity. Reported results<br />show that our proposed framework using RL and CTM in the macroscopic level can<br />computationally efficiently find the proper control solution close to the brute-forcely<br />searched best periodic signal solution (BPSS). For the practical case study conducted<br />by AIMSUN microscopic traffic simulator, the proposed CTM-based RL reveals that<br />the reduction of the average delay can be significantly decreased by 40% with bus<br />lane and 38% without bus lane in comparison with the case of currently used traffic<br />signal strategy. Therefore, the CTM-based RL algorithm could be a useful tool to<br />adjust the proper traffic signal light in practice.
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Van Acker, Jos T., Joris R. Delanghe, Michel R. Langlois, Youri E. Taes, Marc L. De Buyzere, and Alain G. Verstraete. "Automated Flow Cytometric Analysis of Cerebrospinal Fluid." Clinical Chemistry 47, no. 3 (March 1, 2001): 556–60. http://dx.doi.org/10.1093/clinchem/47.3.556.
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Abstract Background: Recently, the UF-100 (Sysmex Corporation) flow cytometer was developed to automate urinalysis. We evaluated the use of flow cytometry in the analysis of cerebrospinal fluid (CSF). Methods: UF-100 data were correlated with microscopy and biochemical data for 256 CSF samples. Microbiological analysis was performed in 144 suspected cases of meningitis. Results: Good agreement was obtained between UF-100 and microscopy data for erythrocytes (r = 0.919) and leukocytes (r = 0.886). In some cases, however, incorrect classification of lymphocytes by the UF-100 led to underestimation of the leukocyte count. UF-100 bacterial count positively correlated (P <0.001) with UF-100 leukocyte count (r = 0.666), CSF total protein (r = 0.754), and CSF lactate concentrations (r = 0.641), and negatively correlated with CSF glucose concentration (r = −0.405; P <0.001). UF-100 bacterial counts were unreliable in hemorrhagic samples and in samples collected by ventricular drainage where interference by blood platelets and cell debris was observed. Another major problem was the UF-100 “bacterial” background signal in sterile CSF samples. Cryptococcus neoformans yeast cells and cholesterol crystals in craniopharyngioma were detected by the flow cytometer. Conclusions: Flow cytometry of CSF with the UF-100 offers a rapid and reliable leukocytes and erythrocyte count. Additional settings offered by the instrument may be useful in the diagnosis of neurological disorders.
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Qin, Chuan Bo, Lian Fang Tian, Qi Liang Du, and Qin Zhang. "Automated Microinjection System Design and Experiments Based on Hybrid-Driven Micro-Robot." Advanced Materials Research 1049-1050 (October 2014): 1116–21. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1116.
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In order to resolve the operation difficulty, high cost problems of traditional manual cell microinjection, a low-cost microinjection system based on hybrid-driven micro-robot is presented. System is mainly composed of microscope, 3-DOF(degrees of freedom) micro-robot, signal generator and power amplifier. Firstly, we describe the automated microinjection system. Secondly, we analyze the motion performance of the robot in the horizontal and vertical direction. The single step resolution is less than 5um each step working at a frequency more than 200Hz. Maximum range of vertical motion is 5mm and minimum resolution ups to 2um. Finally, shrimp egg microinjection experiment is conducted using our system. The experimental result shows that this proposed scheme is simple, reproducible and can achieve microinjection success rate of 88%.
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Ronniger, Michael, Blanche Aguida, Christina Stacke, Yangmengfan Chen, Sabrina Ehnert, Niklas Erdmann, Georg Eschenburg, et al. "A Novel Method to Achieve Precision and Reproducibility in Exposure Parameters for Low-Frequency Pulsed Magnetic Fields in Human Cell Cultures." Bioengineering 9, no. 10 (October 21, 2022): 595. http://dx.doi.org/10.3390/bioengineering9100595.
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The effects of extremely low-frequency electromagnetic field (ELF-MF) exposure on living systems have been widely studied at the fundamental level and also claimed as beneficial for the treatment of diseases for over 50 years. However, the underlying mechanisms and cellular targets of ELF-MF exposure remain poorly understood and the field has been plagued with controversy stemming from an endemic lack of reproducibility of published findings. To address this problem, we here demonstrate a technically simple and reproducible EMF exposure protocol to achieve a standardized experimental approach which can be readily adopted in any lab. As an assay system, we chose a commercially available inflammatory model human cell line; its response to magnetic fields involves changes in gene expression which can be monitored by a simple colorimetric reporter gene assay. The cells were seeded and cultured in microplates and inserted into a custom-built, semi-automated incubation and exposure system which accurately controls the incubation (temperature, humidity, CO2) and magnetic-field exposure conditions. A specific alternating magnetic field (<1.0% spatial variance) including far-field reduction provided defined exposure conditions at the position of each well of the microplate. To avoid artifacts, all environmental and magnetic-field exposure parameters were logged in real time throughout the duration of the experiment. Under these extensively controlled conditions, the effect of the magnetic field on the cell cultures as assayed by the standardized operating procedure was highly reproducible between experiments. As we could fully define the characteristics (frequency, intensity, duration) of the pulsed magnetic field signals at the position of the sample well, we were, for the first time, able to accurately determine the effect of changing single ELF-MF parameters such as signal shape, frequency, intensity and duty cycle on the biological response. One signal in particular (10 Hz, 50% duty cycle, rectangular, bipolar, 39.6μT) provided a significant reduction in cytokine reporter gene expression by 37% in our model cell culture line. In sum, the accuracy, environmental control and data-logging capacity of the semi-automated exposure system should greatly facilitate research into fundamental cellular response mechanisms and achieve the consistency necessary to bring ELF-MF/PEMF research results into the scientific mainstream.
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Gruler, Hans, and Kurt Franke. "Automatic Control and Directed Cell Movement Novel Approach for Understanding Chemotaxis, Galvanotaxis, Galvanotropism." Zeitschrift für Naturforschung C 45, no. 11-12 (December 1, 1990): 1241–49. http://dx.doi.org/10.1515/znc-1990-11-1226.
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Abstract It is shown that chemotaxis, galvanotaxis, galvanotropism , etc. are functions of cells having a goal-seeking system. Even when the involved physicochemical signals are unknown, the cellular system can be treated phenomenologically like an automatic controller having a closed-loop feedback system. The model is verified by means of galvanotaxis and chemotaxis data of human granulocytes. The galvanotaxis and chemotaxis coefficient quantifying the cellular sensibility can be predicted from the coefficient which characterizes the deterministic part of the signal transduction/response system of the cell divided by the coefficient which characterizes the noise strength in the cellular signal transduction/response system. The model is not restricted to directed movement of granulocytes. It is very general and can be applied to any cell type for directed phenomena like chemotaxis, galvanotaxis, phototaxis, magnetotaxis, directed growth, etc. The virus-disturbed directed migration of granulocytes is discussed and it is shown that the virus alters the deterministic part of the cellular controller.
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Perrett, Rebecca M., Robert C. Fowkes, Christopher J. Caunt, Krasimira Tsaneva-Atanasova, Clive G. Bowsher, and Craig A. McArdle. "Signaling to Extracellular Signal-regulated Kinase from ErbB1 Kinase and Protein Kinase C." Journal of Biological Chemistry 288, no. 29 (June 10, 2013): 21001–14. http://dx.doi.org/10.1074/jbc.m113.455345.
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Many extracellular signals act via the Raf/MEK/ERK cascade in which kinetics, cell-cell variability, and sensitivity of the ERK response can all influence cell fate. Here we used automated microscopy to explore the effects of ERK-mediated negative feedback on these attributes in cells expressing endogenous ERK or ERK2-GFP reporters. We studied acute rather than chronic stimulation with either epidermal growth factor (ErbB1 activation) or phorbol 12,13-dibutyrate (PKC activation). In unstimulated cells, ERK-mediated negative feedback reduced the population-average and cell-cell variability of the level of activated ppERK and increased its robustness to changes in ERK expression. In stimulated cells, negative feedback (evident between 5 min and 4 h) also reduced average levels and variability of phosphorylated ERK (ppERK) without altering the “gradedness” or sensitivity of the response. Binning cells according to total ERK expression revealed, strikingly, that maximal ppERK responses initially occur at submaximal ERK levels and that this non-monotonic relationship changes to an increasing, monotonic one within 15 min. These phenomena occur in HeLa cells and MCF7 breast cancer cells and in the presence and absence of ERK-mediated negative feedback. They were best modeled assuming distributive (rather than processive) activation. Thus, we have uncovered a novel, time-dependent change in the relationship between total ERK and ppERK levels that persists without negative feedback. This change makes acute response kinetics dependent on ERK level and provides a “gating” or control mechanism in which the interplay between stimulus duration and the distribution of ERK expression across cells could modulate the proportion of cells that respond to stimulation.
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Stiefel, Janis, Michael Baßler, Jörn Wittek, and Christian Freese. "Automated Immunomagnetic Enrichment and Optomicrofluidic Detection to Isolate Breast Cancer Cells: A Proof-of-Concept towards PoC Therapeutic Decision-Making." Magnetochemistry 8, no. 9 (September 6, 2022): 99. http://dx.doi.org/10.3390/magnetochemistry8090099.
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In breast cancer research, immunomagnetic enrichment of circulating tumor cells (CTCs) from body fluids has impressively evolved over the last decades. However, there is growing interest in further singularizing these pre-enriched rare cells to decrease signal-to-noise ratio for downstream molecular analysis, e.g., to distinguish between hormone receptor-associated tumor subtypes. This can be done by a combinatory principle to link magnetic cell separation with flow cytometry and single cell dispensing. We have recently introduced an automated benchtop platform with a microfluidic disposable cartridge to immunomagnetically enrich, fluorescence-based detect and dispense single cells from biological samples. Herein, we showcase the fine-tuning of microfluidic cell isolation in dependency of bead-binding on the cell surface. We implemented a gating function for the cytometer subunit of the benchtop platform to selectively dispense cells instead of autofluorescent objects. Finally, we developed a simplified qPCR protocol without RNA purification targeting breast cancer-relevant progesterone and estrogen receptor, Muc-1, Her-2, EpCAM and CXCR4 transcripts. In conclusion, the presented results markedly demonstrate a future diagnostic and therapy-accompanying semi-automated workflow using immunomagnetic enrichment, fluorescence-based isolation and dispensing of circulating tumor cells to achieve tumor subtyping by means of rapid, simple and immediate molecular biological examination of single cells.
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Harrison, Reid R., Ilya Kolb, Suhasa B. Kodandaramaiah, Alexander A. Chubykin, Aimei Yang, Mark F. Bear, Edward S. Boyden, and Craig R. Forest. "Microchip amplifier for in vitro, in vivo, and automated whole cell patch-clamp recording." Journal of Neurophysiology 113, no. 4 (February 15, 2015): 1275–82. http://dx.doi.org/10.1152/jn.00629.2014.
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Patch clamping is a gold-standard electrophysiology technique that has the temporal resolution and signal-to-noise ratio capable of reporting single ion channel currents, as well as electrical activity of excitable single cells. Despite its usefulness and decades of development, the amplifiers required for patch clamping are expensive and bulky. This has limited the scalability and throughput of patch clamping for single-ion channel and single-cell analyses. In this work, we have developed a custom patch-clamp amplifier microchip that can be fabricated using standard commercial silicon processes capable of performing both voltage- and current-clamp measurements. A key innovation is the use of nonlinear feedback elements in the voltage-clamp amplifier circuit to convert measured currents into logarithmically encoded voltages, thereby eliminating the need for large high-valued resistors, a factor that has limited previous attempts at integration. Benchtop characterization of the chip shows low levels of current noise [1.1 pA root mean square (rms) over 5 kHz] during voltage-clamp measurements and low levels of voltage noise (8.2 μV rms over 10 kHz) during current-clamp measurements. We demonstrate the ability of the chip to perform both current- and voltage-clamp measurement in vitro in HEK293FT cells and cultured neurons. We also demonstrate its ability to perform in vivo recordings as part of a robotic patch-clamping system. The performance of the patch-clamp amplifier microchip compares favorably with much larger commercial instrumentation, enabling benchtop commoditization, miniaturization, and scalable patch-clamp instrumentation.
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Smith, Elizabeth B., Robert A. Ogert, David Pechter, Artjohn Villafania, Susan J. Abbondanzo, Karen Lin, Aida Rivera-Gines, Cheryl Rebsch-Mastykarz, and Frederick J. Monsma. "HIV Cell Fusion Assay." Journal of Biomolecular Screening 19, no. 1 (August 29, 2013): 108–18. http://dx.doi.org/10.1177/1087057113500074.
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The health and disease-related biology of the CXCR4 chemokine receptor presents the challenge of finding a small molecule that can bind CXCR4 and block T-cell tropic human immunodeficiency virus type 1 (HIV-1) cell entry, while preserving the ability of CXCR4 to respond to its native ligand, CXCL12. HIV entry into the host cell involves the interaction of the viral envelope glycoprotein gp120 binding to CD4, followed by a rearrangement in gp120, and subsequent interaction with the chemokine receptor CXCR4 or CCR5. These initial events can be re-created in a cell fusion assay that represents a surrogate system, mimicking the early stages of viral entry via these host cell receptors. In the current study, a T-tropic HIV cell fusion assay was established using U2OS cells expressing the envelope glycoprotein gp160 from the T-tropic HIV NL4-3 and HeLa cells expressing CD4 and CXCR4. Detection of the cell fusion event was based on a Gal4/VP16-activated β-lactamase signal and was measured by automated microscopy or laser scanning plate cytometry. Changes in morphology associated with cell fusion were combined with β-lactamase activity to generate results with robust assay statistics in both 384-well and 1536-well plates. Compounds were subsequently characterized by CXCR4 signaling assays to eliminate functional antagonists and allow the identification of a function-sparing HIV entry inhibitor.
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Peytavi, Régis, Frédéric R. Raymond, Dominic Gagné, François J. Picard, Guangyao Jia, Jim Zoval, Marc Madou, et al. "Microfluidic Device for Rapid (<15 min) Automated Microarray Hybridization." Clinical Chemistry 51, no. 10 (October 1, 2005): 1836–44. http://dx.doi.org/10.1373/clinchem.2005.052845.
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Abstract Background: Current hybridization protocols on microarrays are slow and need skilled personnel. Microfluidics is an emerging science that enables the processing of minute volumes of liquids to perform chemical, biochemical, or enzymatic analyzes. The merging of microfluidics and microarray technologies constitutes an elegant solution that will automate and speed up microarray hybridization. Methods: We developed a microfluidic flow cell consisting of a network of chambers and channels molded into a polydimethylsiloxane substrate. The substrate was aligned and reversibly bound to the microarray printed on a standard glass slide to form a functional microfluidic unit. The microfluidic units were placed on an engraved, disc-shaped support fixed on a rotational device. Centrifugal forces drove the sample and buffers directly onto the microarray surface. Results: This microfluidic system increased the hybridization signal by ∼10fold compared with a passive system that made use of 10 times more sample. By means of a 15–min automated hybridization process, performed at room temperature, we demonstrated the discrimination of 4 clinically relevant Staphylococcus species that differ by as little as a single-nucleotide polymorphism. This process included hybridization, washing, rinsing, and drying steps and did not require any purification of target nucleic acids. This platform was sensitive enough to detect 10 PCR-amplified bacterial genomes. Conclusion: This removable microfluidic system for performing microarray hybridization on glass slides is promising for molecular diagnostics and gene profiling.
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Schrøder, Rikke L., Søren Friis, Morten Sunesen, Chris Mathes, and Niels J. Willumsen. "Automated Patch-Clamp Technique: Increased Throughput in Functional Characterization and in Pharmacological Screening of Small-Conductance Ca2+ Release-Activated Ca2+ Channels." Journal of Biomolecular Screening 13, no. 7 (July 1, 2008): 638–47. http://dx.doi.org/10.1177/1087057108320274.
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The suitability of an automated patch clamp for the characterization and pharmacological screening of calcium release—activated calcium (CRAC) channels endogenously expressed in RBL-2H3 cells was explored with the QPatch system. CRAC currents (I CRAC) are small, and thus precise recordings require high signal-to-noise ratios obtained by high seal resistances. Automated whole-cell establishment resulted in membrane resistances of 1728 ± 226 MΩ ( n = 44). CRAC channels were activated by a number of methods that raise intracellular calcium concentration, including EGTA, ionomycin, Ins(1,4,5)P3, and thapsigargin. ICRAC whole-cell currents ranged from 30 to 120 pA with rise times of 40 to 150 s. An initial delay in current activation was observed in particular when ICRAC was activated by passive store depletion using EGTA. Apparent rundown of ICRAC was commonly observed, and the current could be reactivated by subsequent addition of thapsigargin. ICRAC was blocked by SKF-96365 and 2-APB with IC50 values of 4.7 ± 1.1 µM ( n = 9) and 7.5 ± 0.7 ( n = 9) µM, respectively. The potencies of these blockers were similar to values reported for ICRAC in similar conventional patch-clamp experiments. The study demonstrates that CRAC channels can be rapidly and efficiently targeted with automated patch-clamp techniques for characterization of physiological and pharmacological properties. ( Journal of Biomolecular Screening 2008:638-647)
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Uslu, Fatma, Kutay Icoz, Kasim Tasdemir, and Bulent Yilmaz. "Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images." Biomedical Signal Processing and Control 49 (March 2019): 473–82. http://dx.doi.org/10.1016/j.bspc.2019.01.002.
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Selvanantham, Thiru, Stephen K. H. Li, Nick Zabinyakov, Alexandre Bouzekri, Raymond Jong, Michael Sullivan, Alexander Laboda, Daniel Majonis, and Christina Loh. "Abstract 3908: A streamlined and automated approach to high-content cytometric immunophenotyping with CyTOF XT." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3908. http://dx.doi.org/10.1158/1538-7445.am2022-3908.
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Abstract High-parameter immune profiling is crucial in translational and clinical research to quantify changes in immune cell populations over time. CyTOF® mass cytometry is a high-plex, single-cell analysis platform that uses isotopically pure metal-labeled antibodies. The major advantage of CyTOF is its ability to resolve 40-plus markers in a single panel without compensation, making mass cytometry ideal for routine immunophenotyping. The autosampler module of CyTOF XT™ provides significant time savings by allowing automated sample acquisition. Tubes of pelleted stained samples are loaded into the Autosampler carousel, and the samples are resuspended with EQ™ Calibration Beads for acquisition. User input is only required during instrument startup, tuning, and batch setup. The added automation of CyTOF XT provides a streamlined workflow for suspension mass cytometry. Testing was performed to ensure that the data obtained on CyTOF XT was comparable to manual acquisition systems. The performance of CyTOF XT was tested in parallel with its predecessor, Helios™. Several workflows and applications for suspension mass cytometry including sample barcoding with the Cell-ID™ 20-Plex Pd Barcoding Kit, and surface, cytoplasmic, and nuclear staining and phosphostaining were evaluated on human PBMC. Manual gating analysis was performed to assess population frequencies and median intensities for each marker. Resolution index was calculated to assess how well positive and negative populations separated from each other. There was no significant difference between population frequencies analyzed between the two CyTOF systems. Moreover, samples acquired on CyTOF XT, on average, resulted in greater signal resolution between positive and negative populations compared to Helios. The Maxpar Direct Immune Profiling System was also compared on CyTOF XT and Helios using human whole blood and PBMC. The Maxpar® Direct™ Immune Profiling Assay™ and Maxpar Pathsetter™ software were developed as a sample-to-answer system for human immune profiling using CyTOF. The Maxpar Direct Immune Profiling Assay includes an optimized panel of 30 unique markers in a dry, single-tube format. Maxpar Pathsetter is an automated software used to report population statistics, stain assessments, and relevant data plots for the panel. The automated staining assessment in Maxpar Pathsetter was compared between files acquired on CyTOF XT and Helios. Comparable population frequencies were obtained between the two acquisition systems, and improved staining assessment was observed on CyTOF XT. Overall, these studies demonstrate that CyTOF XT generates better signal resolution as compared to Helios. The automated acquisition of CyTOF XT enables researchers to streamline immunophenotyping of human samples while accurately and reproducibly monitoring changes in immune cell subsets. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Thiru Selvanantham, Stephen K.H. Li, Nick Zabinyakov, Alexandre Bouzekri, Raymond Jong, Michael Sullivan, Alexander Laboda, Daniel Majonis, Christina Loh. A streamlined and automated approach to high-content cytometric immunophenotyping with CyTOF XT [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 3908.
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Hiroshima, Michio, Masato Yasui, and Masahiro Ueda. "Large-scale single-molecule imaging aided by artificial intelligence." Microscopy 69, no. 2 (February 22, 2020): 69–78. http://dx.doi.org/10.1093/jmicro/dfz116.
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Abstract Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena, single-molecule imaging analysis has not been extended to a large scale of molecules in cells due to the low measurement throughput as well as required expertise. To overcome these problems, we have automated the imaging processes by using computer operations, robotics and artificial intelligence (AI). AI is an ideal substitute for expertise to obtain high-quality images for quantitative analysis. Our automated in-cell single-molecule imaging system, AiSIS, could analyze 1600 cells in 1 day, which corresponds to ∼ 100-fold higher efficiency than manual analysis. The large-scale analysis revealed cell-to-cell heterogeneity in the molecular behavior, which had not been recognized in previous studies. An analysis of the receptor behavior and downstream signaling was accomplished within a significantly reduced time frame and revealed the detailed activation scheme of signal transduction, advancing cell biology research. Furthermore, by combining the high-throughput analysis with our previous finding that a receptor changes its behavioral dynamics depending on the presence of a ligand/agonist or inhibitor/antagonist, we show that AiSIS is applicable to comprehensive pharmacological analysis such as drug screening. This AI-aided automation has wide applications for single-molecule analysis.
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Salles, Audrey, Cyrille Billaudeau, Arnauld Sergé, Anne-Marie Bernard, Marie-Claire Phélipot, Nicolas Bertaux, Mathieu Fallet, et al. "Barcoding T Cell Calcium Response Diversity with Methods for Automated and Accurate Analysis of Cell Signals (MAAACS)." PLoS Computational Biology 9, no. 9 (September 26, 2013): e1003245. http://dx.doi.org/10.1371/journal.pcbi.1003245.
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Miller, Brandon L., Clayton Deighan, Jeffrey J. Chalmers, and Maryam B. Lustberg. "Development of automated quantitative multiparameter immunocytochemical profiling of circulating tumor cells (CTCs) in breast cancer (BC)." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e21124-e21124. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e21124.
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e21124 Background: The currently accepted definition of a CTC is a nucleated cell that is positive by qualitative fluorescence for EpCAM, expresses cytokeratin, and is negative for the pan-hematopoietic marker CD45. Such approach has limitations such as operator bias, difficulty distinguishing true signal from autofluoresence or filter bleed-through, and most importantly, it cannot differentiate expression levels that are low, intermediate or high. This is a problem because CTCs are in fact heterogeneous with respect to phenotypic expression of various markers. Methods: Peripheral blood samples were subjected to our previously described, negative depletion technology. Cytospins of enriched samples were stained with 3 to 5 antibodies conjugated to various Alexa Fluor dyes plus a nuclear dye and then analyzed with both confocal microscopy and a traditional epifluorescence microscope equipped with a Nuance (CRi) multispectral imaging camera and associated software. This imaging system allows not only fluorescent signals to be quantified between specific excitation and emission filters, but also the spectral structure between these boundaries through the use of a liquid crystal that takes images in as little as 10nm increments. Such imaging allows spectra from specific dyes to identify from the contributions of other dyes, filter leakage, and auto fluorescence. Results: Using this technology, and appropriate controls, we are developing continuous variable quantification of the expression of: cytokeratins, vimentin, CD45, Her2, CD44, EpCAM and EGFR in negatively enriched, peripheral blood samples from metastatic breast cancer patients. To date, over 10 metastatic patient samples have been quantified and the study is ongoing. Initial quantification indicates that significant variability in expression levels exists in multiple markers including Her2 neu. Conclusions: Using multispectral technology, we have shown that CTCs are heterogeneous, with different CTCs having varying expression levels of EpCAM, HER2 neu, vimentin and EGFR. Further multiparameter quantitative characterization with up to 6 color analysis on the same cell is in progress.
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Pan, Yu-Xi, Da-Wei Li, Yun Duan, Zhi-Zhou Zhang, Ming-Qing Xu, Guo-Yin Feng, and Lin He. "Predicting Protein Subcellular Location Using Digital Signal Processing." Acta Biochimica et Biophysica Sinica 37, no. 2 (February 1, 2005): 88–96. http://dx.doi.org/10.1093/abbs/37.2.88.
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Abstract The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.
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Chaudry, Qaiser, Syed Hussain Raza, Andrew N. Young, and May D. Wang. "Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features." Journal of Signal Processing Systems 55, no. 1-3 (June 21, 2008): 15–23. http://dx.doi.org/10.1007/s11265-008-0214-6.
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TKACZYK, ALAN H., ERIC R. TKACZYK, THEODORE B. NORRIS, and SHUICHI TAKAYAMA. "MICROFLUIDIC DROPLET CONSISTENCY MONITORING AND ENCAPSULATED CELL DETECTION VIA LASER EXCITATION." Journal of Mechanics in Medicine and Biology 11, no. 01 (March 2011): 1–14. http://dx.doi.org/10.1142/s0219519410003617.
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Microfluidic droplets formed in emulsions are used in a variety of analytical techniques and hold great potential for future scientific and commercial applications. Our experiments present a microdroplet generation and consistency monitoring system with laser optics excitation and detection. We also demonstrate the detection of cancer cells encapsulated within aqueous microdroplets in continuous oil phase flow. The custom setup analyzes each droplet with sub-millisecond signal resolution and single photon accuracy, and is compatible with process-monitoring methods. To demonstrate the consistency of microdroplet generation over time, we measure and examine the mean frequency of aqueous plug-shaped droplet (microplug) formation in oil phase, as well as the mean length and interval between consecutive droplets. Two-channel optical monitoring allows for the simultaneous and independent inspection of both microdroplet generation and identification of green fluorescent protein-labeled cancer cells within the droplets. A precise, quantitative approach as utilized in these experiments may be helpful in the development of microfluidic concepts that require exacting reproducibility and would benefit from automated consistency monitoring techniques.
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Saha, Tanumoy, Isabel Rathmann, Abhiyan Viplav, Sadhana Panzade, Isabell Begemann, Christiane Rasch, Jürgen Klingauf, Maja Matis, and Milos Galic. "Automated analysis of filopodial length and spatially resolved protein concentration via adaptive shape tracking." Molecular Biology of the Cell 27, no. 22 (November 7, 2016): 3616–26. http://dx.doi.org/10.1091/mbc.e16-06-0406.
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Filopodia are dynamic, actin-rich structures that transiently form on a variety of cell types. To understand the underlying control mechanisms requires precise monitoring of localization and concentration of individual regulatory and structural proteins as filopodia elongate and subsequently retract. Although several methods exist that analyze changes in filopodial shape, a software solution to reliably correlate growth dynamics with spatially resolved protein concentration along the filopodium independent of bending, lateral shift, or tilting is missing. Here we introduce a novel approach based on the convex-hull algorithm for parallel analysis of growth dynamics and relative spatiotemporal protein concentration along flexible filopodial protrusions. Detailed in silico tests using various geometries confirm that our technique accurately tracks growth dynamics and relative protein concentration along the filopodial length for a broad range of signal distributions. To validate our technique in living cells, we measure filopodial dynamics and quantify spatiotemporal localization of filopodia-associated proteins during the filopodial extension–retraction cycle in a variety of cell types in vitro and in vivo. Together these results show that the technique is suitable for simultaneous analysis of growth dynamics and spatiotemporal protein enrichment along filopodia. To allow readily application by other laboratories, we share source code and instructions for software handling.
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Soikkeli, Anne, Cristina Sempio, Ann Marie Kaukonen, Arto Urtti, Jouni Hirvonen, and Marjo Yliperttula. "Feasibility Evaluation of 3 Automated Cellular Drug Screening Assays on a Robotic Workstation." Journal of Biomolecular Screening 15, no. 1 (November 25, 2009): 30–41. http://dx.doi.org/10.1177/1087057109352236.
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This study presents the implementation and optimization of 3 cell-based assays on a TECAN Genesis workstation—the Caspase-Glo® 3/7 and sulforhodamine B (SRB) screening assays and the mechanistic Caco-2 permeability protocol—and evaluates their feasibility for automation. During implementation, the dispensing speed to add drug solutions and fixative trichloroacetic acid and the aspiration speed to remove the supernatant immediately after fixation were optimized. Decontamination steps for cleaning the tips and pipetting tubing were also added. The automated Caspase-Glo® 3/7 screen was successfully optimized with Caco-2 cells (Z′ 0.7, signal-to-base ratio [S/B] 1.7) but not with DU-145 cells. In contrast, the automated SRB screen was successfully optimized with the DU-145 cells (Z′ 0.8, S/B 2.4) but not with the Caco-2 cells (Z′ —0.8, S/B 1.4). The automated bidirectional Caco-2 permeability experiments separated successfully low- and high-permeability compounds (Z′ 0.8, S/B 84.2) and passive drug permeation from efflux-mediated transport (Z′ 0.5, S/B 8.6). Of the assays, the homogeneous Caspase-Glo® 3/7 assay benefits the most from automation, but also the heterogeneous SRB assay and Caco-2 permeability experiments gain advantages from automation.
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Zarubin, Dmitry, Valeriy Belotskiy, Zhongmin Xiang, Arina Varlamova, Pavel Ovcharov, Ilia Galkin, Margarita Polyakova, et al. "A clinical AI-driven multiplex immunofluorescence imaging pipeline to characterize tumor microenvironment heterogeneity." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3020. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3020.
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3020 Background: Understanding the underlying heterogeneity of the tumor microenvironment (TME) on a single-cell level is becoming increasingly important to predict a patient’s response to immunotherapy. Conventional imaging methods can help reveal tissue heterogeneity, but are not optimal for identifying multiple cellular subpopulations or cellular interactions from a single slide image, limiting their use in clinical settings. Here, we present a clinical artificial intelligence (AI)-driven multiplex immunofluorescence (MxIF) imaging pipeline based on novel cell segmentation and cell typing methods to evaluate tumor cellular heterogeneity, immune cell composition, and cell-to-cell interactions. Methods: A machine learning (ML)-based cell segmentation algorithm was trained on a manually annotated dataset created from 219 different regions of interest (ROIs) that contained 85,991 cells from various tissues (colon, kidney, lung, lymph node, tonsil, and ureter). A dataset containing 58,676 cells from 146 ROIs was used for validation and accuracy was determined between automated and manually annotated images; accuracy was further evaluated by calculating the f1-score using available methods (DeepCell and Stardist). Marker stains with a low signal-to-noise ratio were automatically enhanced, allowing for adequate cell-to-cell interaction analysis. Results: An automated MxIF image processing workflow was developed. Validation of the trained cell segmentation model showed high accuracy (0.80 f1-score), demonstrating superior performance compared to other methods (DeepCell and Stardist - 0.55 and 0.78 f1-score, respectively). The pathologist-determined accuracy (0.84 mean f1-score) indicated a near-human performance of the developed method. Normalized expression values obtained from the cell typing model allowed automated cell recognition. We analyzed cellular heterogeneity across 3 regions of colorectal cancer (CRC), gastric cancer (GC), and non-small cell lung cancer (NSCLC) samples. While proportions of immune cells varied, proportions of malignant epithelial cells were stable across all regions of each sample, as concordant percentages of Ki67+ cells were identified (CRC-19%; GC-21%; NSCLC-5%). Analysis of cell-to-cell interactions and immune communities identified tumor-, immune-, and stromal-enriched communities in all tumor samples that were stable across regions. Conclusions: By analyzing complex tumor tissue at single-cell resolution with high accuracy, this AI-driven MxIF imaging technology is able to characterize tumor and microenvironment heterogeneity across cancer types. This novel AI-based tool is currently being integrated into several ongoing prospective clinical studies to aid in the development of predictive and prognostic biomarkers.
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Zarubin, Dmitry, Valeriy Belotskiy, Zhongmin Xiang, Arina Varlamova, Pavel Ovcharov, Ilia Galkin, Margarita Polyakova, et al. "A clinical AI-driven multiplex immunofluorescence imaging pipeline to characterize tumor microenvironment heterogeneity." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3020. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3020.
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3020 Background: Understanding the underlying heterogeneity of the tumor microenvironment (TME) on a single-cell level is becoming increasingly important to predict a patient’s response to immunotherapy. Conventional imaging methods can help reveal tissue heterogeneity, but are not optimal for identifying multiple cellular subpopulations or cellular interactions from a single slide image, limiting their use in clinical settings. Here, we present a clinical artificial intelligence (AI)-driven multiplex immunofluorescence (MxIF) imaging pipeline based on novel cell segmentation and cell typing methods to evaluate tumor cellular heterogeneity, immune cell composition, and cell-to-cell interactions. Methods: A machine learning (ML)-based cell segmentation algorithm was trained on a manually annotated dataset created from 219 different regions of interest (ROIs) that contained 85,991 cells from various tissues (colon, kidney, lung, lymph node, tonsil, and ureter). A dataset containing 58,676 cells from 146 ROIs was used for validation and accuracy was determined between automated and manually annotated images; accuracy was further evaluated by calculating the f1-score using available methods (DeepCell and Stardist). Marker stains with a low signal-to-noise ratio were automatically enhanced, allowing for adequate cell-to-cell interaction analysis. Results: An automated MxIF image processing workflow was developed. Validation of the trained cell segmentation model showed high accuracy (0.80 f1-score), demonstrating superior performance compared to other methods (DeepCell and Stardist - 0.55 and 0.78 f1-score, respectively). The pathologist-determined accuracy (0.84 mean f1-score) indicated a near-human performance of the developed method. Normalized expression values obtained from the cell typing model allowed automated cell recognition. We analyzed cellular heterogeneity across 3 regions of colorectal cancer (CRC), gastric cancer (GC), and non-small cell lung cancer (NSCLC) samples. While proportions of immune cells varied, proportions of malignant epithelial cells were stable across all regions of each sample, as concordant percentages of Ki67+ cells were identified (CRC-19%; GC-21%; NSCLC-5%). Analysis of cell-to-cell interactions and immune communities identified tumor-, immune-, and stromal-enriched communities in all tumor samples that were stable across regions. Conclusions: By analyzing complex tumor tissue at single-cell resolution with high accuracy, this AI-driven MxIF imaging technology is able to characterize tumor and microenvironment heterogeneity across cancer types. This novel AI-based tool is currently being integrated into several ongoing prospective clinical studies to aid in the development of predictive and prognostic biomarkers.
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Yang, Jun, Jing Yang, Qing He You, Ning Hu, Yong Li, Jie Chen, Ting Yu Li, Jing Xu, and Yi Cao. "Design and Performance of a High-Efficiency Cell-Electrofusion Device." Applied Mechanics and Materials 52-54 (March 2011): 664–67. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.664.
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A microfluidic cell-electrofusion device was developed for high fusion efficiency and automatic manipulation. It included an experimental platform, an electric signal generator and a microchip. Microstructure and controlling signal were elaborately chosen in order to realize precise cell manipulation, alignment and electrofusion. Compared with the traditional cell-electrofusion device, this device has some outstanding advantages. For example, short distance between two counter-microelectrodes will reduce the required voltage. A large number of microelectrodes can be fabricated on one small chip for high-throughput fusion. Transparent chip architecture benefited for microscopic observation and real-time recording. The integration of the experimental platform, signal generator and microchip is helpful for developing a portable cell-fusion system. On-chip electrofusion experiments of cucumber mesophyll protoplasts have been carried out and up to 40% fusion efficiency was obtained.
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Oyaert, Matthijs N., Jonas Himpe, Marijn M. Speeckaert, Veronique V. Stove, and Joris R. Delanghe. "Quantitative urine test strip reading for leukocyte esterase and hemoglobin peroxidase." Clinical Chemistry and Laboratory Medicine (CCLM) 56, no. 7 (June 27, 2018): 1126–32. http://dx.doi.org/10.1515/cclm-2017-1159.
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Abstract Background: Recently, urine test strip readers have become available for automated test strip analysis. We explored the possibilities of the Sysmex UC-3500 automated urine chemistry analyzer based on complementary metal oxide semiconductor (CMOS) sensor technology with regard to accuracy of leukocyte esterase and hemoglobin peroxidase results. We studied the influence of possible confounders on these measurements. Methods: Reflectance data of leukocyte esterase and hemoglobin peroxidase were measured using CMOS technology on the Sysmex UC-3500 automated urine chemistry analyzer. Analytical performance (imprecision, LOQ) as well as the correlation with white blood cell (WBC) and red blood cell (RBC) counts (Sysmex UF-5000) were studied. Furthermore, the influence of urinary dilution, haptoglobin, pH and ascorbic acid as confounders was determined. Results: Within- and between-run imprecision (reflectance signal) ranged from 1.1% to 3.6% and 0.9% to 4.2% for peroxidase and 0.4% to 2.5% and 0.4% to 3.3% for leukocyte esterase. Good agreement was obtained between the UF-5000 for RBCs and peroxidase reflectance (r=0.843) and for WBCs and leukocyte esterase (r=0.821). Specific esterase activity decreased for WBC counts exceeding 100 cells/μL. Haptoglobin influenced the peroxidase activity, whereas leukocyte esterase and peroxidase activities showed a pH optimum between 5.0 and 6.5. A sigmoidal correlation was observed between urinary osmolality and peroxidase activity. Conclusions: CMOS technology allows to obtain high quality test strip results for assessing WBC and RBC in urine. Quantitative peroxidase and leukocyte esterase are complementary with flow cytometry and have an added value in urinalysis, which may form a basis for expert system development.
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Kayali, S., H. Ancin, B. Roysam, D. H. Szarowski, W. Shain, and J. N. Turner. "Corrections for Several Factors that Limit Quantitative Analysis Of 3-D Data Sets Collected using Scanning-Laser Confocal Microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 462–63. http://dx.doi.org/10.1017/s0424820100164775.
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The confocal microscope collects high contrast 2-D images at different depths from thick (≥100 μm) tissues samples, thus providing data for quantitative measurements of 3-D objects. There are several factors that may limit quanititative analysis including photobleaching of fluors, attenuation of measured signal, mismatch in the various refractive indeces of the sample, and the width of the point spread function. We have addressed the first three of these limitations by using acriflavine-stained 100- [im thick sections of rat hippocampus and our 3-D automated analysis software. The laminar structure of the hippocampus provides tissue sections with areas of high and low nuclear density (pyramidal and extra-pyradmidal cell regions, respectively). Corrections for these limitations were made to samples from rat testes where developing spermatogonia provide nuclei of different DNA content.The contribution of photobleaching to signal degradation was determined by collecting multiple 3-D images (z-series) of the same field. Twenty-one images of 50 planes each were collected (total scans= 1050).