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Trends in Cell Biology Mar 2020Cell density shows very little variation within a given cell type. For example, in humans variability in cell density among cells of a given cell type is 100 times... (Review)
Review
Cell density shows very little variation within a given cell type. For example, in humans variability in cell density among cells of a given cell type is 100 times smaller than variation in cell mass. This tight control indicates that maintenance of a cell type-specific cell density is important for cell function. Indeed, pathological conditions such as cellular senescence are accompanied by changes in cell density. Despite the apparent importance of cell-type-specific density, we know little about how cell density affects cell function, how it is controlled, and how it sometimes changes as part of a developmental process or in response to changes in the environment. The recent development of new technologies to accurately measure the cell density of single cells in suspension and in tissues is likely to provide answers to these important questions.
Topics: Animals; Cell Count; Cells; Cytological Techniques; Humans; Models, Biological
PubMed: 31980346
DOI: 10.1016/j.tcb.2019.12.006 -
The Journal of Comparative Neurology Dec 2016For half a century, the human brain was believed to contain about 100 billion neurons and one trillion glial cells, with a glia:neuron ratio of 10:1. A new counting... (Review)
Review
For half a century, the human brain was believed to contain about 100 billion neurons and one trillion glial cells, with a glia:neuron ratio of 10:1. A new counting method, the isotropic fractionator, has challenged the notion that glia outnumber neurons and revived a question that was widely thought to have been resolved. The recently validated isotropic fractionator demonstrates a glia:neuron ratio of less than 1:1 and a total number of less than 100 billion glial cells in the human brain. A survey of original evidence shows that histological data always supported a 1:1 ratio of glia to neurons in the entire human brain, and a range of 40-130 billion glial cells. We review how the claim of one trillion glial cells originated, was perpetuated, and eventually refuted. We compile how numbers of neurons and glial cells in the adult human brain were reported and we examine the reasons for an erroneous consensus about the relative abundance of glial cells in human brains that persisted for half a century. Our review includes a brief history of cell counting in human brains, types of counting methods that were and are employed, ranges of previous estimates, and the current status of knowledge about the number of cells. We also discuss implications and consequences of the new insights into true numbers of glial cells in the human brain, and the promise and potential impact of the newly validated isotropic fractionator for reliable quantification of glia and neurons in neurological and psychiatric diseases. J. Comp. Neurol. 524:3865-3895, 2016. © 2016 Wiley Periodicals, Inc.
Topics: Animals; Brain; Cell Count; History, 19th Century; History, 20th Century; History, 21st Century; Humans; Neuroglia; Neurons
PubMed: 27187682
DOI: 10.1002/cne.24040 -
Proceedings of the National Academy of... Jul 2011We have used a microfluidic mass sensor to measure the density of single living cells. By weighing each cell in two fluids of different densities, our technique measures...
We have used a microfluidic mass sensor to measure the density of single living cells. By weighing each cell in two fluids of different densities, our technique measures the single-cell mass, volume, and density of approximately 500 cells per hour with a density precision of 0.001 g mL(-1). We observe that the intrinsic cell-to-cell variation in density is nearly 100-fold smaller than the mass or volume variation. As a result, we can measure changes in cell density indicative of cellular processes that would be otherwise undetectable by mass or volume measurements. Here, we demonstrate this with four examples: identifying Plasmodium falciparum malaria-infected erythrocytes in a culture, distinguishing transfused blood cells from a patient's own blood, identifying irreversibly sickled cells in a sickle cell patient, and identifying leukemia cells in the early stages of responding to a drug treatment. These demonstrations suggest that the ability to measure single-cell density will provide valuable insights into cell state for a wide range of biological processes.
Topics: Anemia, Sickle Cell; Animals; Blood Transfusion; Cell Count; Cell Size; Erythrocytes; Erythrocytes, Abnormal; Humans; Leukemia L1210; Malaria, Falciparum; Microfluidic Analytical Techniques
PubMed: 21690360
DOI: 10.1073/pnas.1104651108 -
Biotechnology Advances Nov 2022One of the main challenges in the development of bioprocesses based on cell transient expression is the commonly reported reduction of cell specific productivity at... (Review)
Review
One of the main challenges in the development of bioprocesses based on cell transient expression is the commonly reported reduction of cell specific productivity at increasing cell densities. This is generally known as the cell density effect (CDE). Many efforts have been devoted to understanding the cell metabolic implications to this phenomenon in an attempt to design operational strategies to overcome it. A comprehensive analysis of the main studies regarding the CDE is provided in this work to better define the elements comprising its cause and impact. Then, examples of methodologies and approaches employed to achieve successful transient expression at high cell densities (HCD) are thoroughly reviewed. A critical assessment of the limitations of the reported studies in the understanding of the CDE is presented, covering the leading hypothesis of the molecular implications. The overall analysis of previous work on CDE may offer useful insights for further research into manufacturing of biologics.
Topics: Animals; Biological Products; Cell Count
PubMed: 35809763
DOI: 10.1016/j.biotechadv.2022.108017 -
IEEE Transactions on Bio-medical... Oct 2021In biomanufacturing there is a need for quantitative methods to map cell viability and density inside 3D bioreactors to assess health and proliferation over time....
OBJECTIVE
In biomanufacturing there is a need for quantitative methods to map cell viability and density inside 3D bioreactors to assess health and proliferation over time. Recently, noninvasive MRI readouts of cell density have been achieved. However, the ratio of live to dead cells was not varied. Herein we present an approach for measuring the viability of cells embedded in a hydrogel independently from cell density to map cell number and health.
METHODS
Independent quantification of cell viability and density was achieved by calibrating the H magnetization transfer- (MT) and diffusion-weighted NMR signals to samples of known cell density and viability using a multivariate approach. Maps of cell viability and density were generated by weighting NMR images by these parameters post-calibration.
RESULTS
Using this method, the limits of detection (LODs) of total cell density and viable cell density were found to be 3.88 ×10 cells · mL · Hz and 2.36 ×10 viable cells · mL · Hz respectively.
CONCLUSION
This mapping technique provides a noninvasive means of visualizing cell viability and number density within optically opaque bioreactors.
SIGNIFICANCE
We anticipate that such nondestructive readouts will provide valuable feedback for monitoring and controlling cell populations in bioreactors.
Topics: Cell Count; Cell Survival; Hydrogels; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy
PubMed: 33531296
DOI: 10.1109/TBME.2021.3056526 -
Cells Aug 2022Quorum sensing (QS) was historically described as a mechanism by which bacteria detect and optimize their population density via gene regulation based on dynamic... (Review)
Review
Quorum sensing (QS) was historically described as a mechanism by which bacteria detect and optimize their population density via gene regulation based on dynamic environmental cues. Recently, it was proposed that QS or similar mechanisms may have broader applications across different species and cell types. Indeed, emerging evidence shows that the mammalian immune system can also elicit coordinated responses on a population level to regulate cell density and function, thus suggesting that QS-like mechanisms may also be a beneficial trait of the immune system. In this review, we explore and discuss potential QS-like mechanisms deployed by the immune system to coordinate cellular-level responses, such as T cell responses mediated via the common gamma chain (γc) receptor cytokines and the aryl hydrocarbon receptors (AhRs). We present evidence regarding a novel role of QS as a multifunctional mechanism coordinating CD4 and CD8 T cell behavior during steady state and in response to infection, inflammatory diseases, and cancer. Successful clinical therapies such as adoptive cell transfer for cancer treatment may be re-evaluated to harness the effects of the QS mechanism(s) and enhance treatment responsiveness. Moreover, we discuss how signaling threshold perturbations through QS-like mediators may result in disturbances of the complex crosstalk between immune cell populations, undesired T cell responses, and induction of autoimmune pathology. Finally, we discuss the potential therapeutic role of modulating immune-system-related QS as a promising avenue to treat human diseases.
Topics: Animals; Bacteria; Cell Count; Humans; Immune System; Mammals; Quorum Sensing; Signal Transduction
PubMed: 35954285
DOI: 10.3390/cells11152442 -
Scientific Reports Jan 2023Optical density at 600 nm (OD) measurements are routinely and quickly taken to estimate cell density in cultivation and to track cell growth. The yeast Saccharomyces...
Optical density at 600 nm (OD) measurements are routinely and quickly taken to estimate cell density in cultivation and to track cell growth. The yeast Saccharomyces cerevisiae is one of the microorganisms most used in industry, and the OD values are frequently adopted as the indicator of yeast cell density, according to the Beer-Lambert law. Because the OD value is based on turbidity measurement, the Beer-Lambert law can be applied only for microbial cultivation with low cell densities. The proportionality constants strongly depend on several parameters such as cell size. Typically, yeast strains are categorized into haploids and diploids. It is well known that cell size of diploid yeasts is larger than haploid cells. Additionally, polyploid (especially triploid and tetraploid) yeast cells are also employed in several human-activities such as bread-making and lager-brewing. As a matter of fact, there is almost no attention paid to the difference in the proportionality constants depending on the yeast ploidy. This study presents information for cell size of haploid, diploid, triploid, and tetraploid yeasts with isogenic background, and describes their proportionality constants (k) corresponding to the molar extinction coefficient (ε) in the Beer-Lambert law. Importantly, it was found that the constants are inversely proportional to apparent cell diameters estimated by flow cytometric analysis. Although each cell property highly depends on genetic and environmental factors, a set of results obtained from yeast strains with different ploidy in the current study would serve as a major reference source for researchers and technical experts.
Topics: Humans; Saccharomyces cerevisiae; Tetraploidy; Triploidy; Haploidy; Cell Count; Fermentation
PubMed: 36707648
DOI: 10.1038/s41598-023-28800-z -
ENeuro 2021Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience research, enabling a greater understanding of...
Tracking and quantifying the abundance and location of cells in the developing brain is essential in neuroscience research, enabling a greater understanding of mechanisms underlying nervous system morphogenesis. Widely used experimental methods to quantify cells labeled with fluorescent markers, such as immunohistochemistry (IHC), hybridization, and expression of transgenes via stable lines or transient electroporations (IUEs), depend on accurate and consistent quantification of images. Current methods to quantify fluorescently-labeled cells rely on labor-intensive manual counting approaches, such as the Fiji plugin , which requires custom macros to enable higher-throughput analyses. Here, we present RapID Cell Counter, a semi-automated cell-counting tool with an easy-to-implement graphical user interface (GUI), which facilitates quick and consistent quantifications of cell density within user-defined boundaries that can be divided into equally-partitioned segments. Compared with the standard manual counting approach, we show that RapID matched accuracy and consistency and only required ∼10% of user time relative to manual counting methods, when quantifying the distribution of fluorescently-labeled neurons in mouse IUE experiments. Using RapID, we recapitulated previously published work focusing on two genes, and , important for projection neuron (PN) migration in the neocortex and used it to quantify PN displacement in a mouse knock-out model of Moreover, RapID is capable of quantifying other cell types in the brain with complex cell morphologies, including astrocytes and dopaminergic neurons. We propose RapID as an efficient method for neuroscience researchers to process fluorescently-labeled brain images in a consistent, accurate, and mid-throughput manner.
Topics: Animals; Astrocytes; Cell Count; Computer Graphics; GTPase-Activating Proteins; Mice; Morphogenesis; Neocortex; Neurons; User-Computer Interface
PubMed: 34725102
DOI: 10.1523/ENEURO.0185-21.2021 -
PeerJ 2022Cell density signaling drives tendon morphogenesis by regulating both procollagen production and cell proliferation. The signal is composed of a small, highly conserved...
A synthetic cell density signal can drive proliferation in chick embryonic tendon cells and tendon cells from a full size rooster can produce high levels of procollagen in cell culture.
Cell density signaling drives tendon morphogenesis by regulating both procollagen production and cell proliferation. The signal is composed of a small, highly conserved protein (SNZR P) tightly bound to a tissue-specific, unique lipid (SNZR L). This allows the complex (SNZR PL) to bind to the membrane of the cell and locally diffuse over a radius of ~1 mm. The cell produces low levels of this signal but the binding to the membrane increases with the number of tendon cells in the local environment. In this article SNZR P was produced in and SNZR L was chemically synthesized. The two bind together when heated to 60 °C in the presence of Ca and Mg and the synthesized SNZR PL at ng/ml levels can replace serum. Adding SNZR PL to the medium was also tested on primary tendon cells from adult roosters. The older cells were in a maintenance state and in cell culture they proliferate more slowly than embryonic cells. Nevertheless, after reaching a moderately high cell density, they produced high levels of procollagen similar to the embryonic cells. This data was not expected from older cells but suggests that adult tendon cells can regenerate the tissue after injury when given the correct signals.
Topics: Animals; Male; Procollagen; Chickens; Artificial Cells; Tendons; Cell Proliferation; Cell Culture Techniques; Cell Count
PubMed: 36530397
DOI: 10.7717/peerj.14533 -
Development, Growth & Differentiation May 2011The social amoeba Dictyostelium discoideum is one of the leading model systems used to study how cells count themselves to determine the number and/or density of cells.... (Review)
Review
The social amoeba Dictyostelium discoideum is one of the leading model systems used to study how cells count themselves to determine the number and/or density of cells. In this review, we describe work on three different cell-density sensing systems used by Dictyostelium. The first involves a negative feedback loop in which two secreted signals inhibit cell proliferation during the growth phase. As the cell density increases, the concentrations of the secreted factors concomitantly increase, allowing the cells to sense their density. The two signals act as message authenticators for each other, and the existence of two different signals that require each other for activity may explain why previous efforts to identify autocrine proliferation-inhibiting signals in higher eukaryotes have generally failed. The second system involves a signal made by growing cells that is secreted only when they starve. This then allows cells to sense the density of just the starving cells, and is an example of a mechanism that allows cells in a tissue to sense the density of one specific cell type. The third cell density counting system involves cells in aggregation streams secreting a signal that limits the size of fruiting bodies. Computer simulations predicted, and experiments then showed, that the factor increases random cell motility and decreases cell-cell adhesion to cause streams to break up if there are too many cells in the stream. Together, studies on Dictyostelium cell density counting systems will help elucidate how higher eukaryotes regulate the size and composition of tissues.
Topics: Cell Count; Cell Proliferation; Cell Size; Cyclic AMP; Dictyostelium; Protozoan Proteins; Quorum Sensing; Signal Transduction
PubMed: 21521184
DOI: 10.1111/j.1440-169X.2010.01248.x