scholarly journals Analysis of variability in estimates of cell proliferation parameters for cyton-based models using CFSE-based flow cytometry data

2015 ◽  
Vol 23 (2) ◽  
Author(s):  
H. Thomas Banks ◽  
Dustin F. Kapraun ◽  
Kathryn G. Link ◽  
W. Clayton Thompson ◽  
Cristina Peligero ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Proliferation ◽  
Large Body ◽  
Cell Types ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Flow Cytometry Data ◽  
Healthy Donors ◽  
Estimation Scheme ◽  
Cyton Model

AbstractIn this article we assess variability in cell proliferation dynamics observed for CD4+ and CD8+ T cells collected from two healthy donors. We review a recently developed class of models that incorporates the so-called “cyton model” for cell numbers into a conservation-based PDE model for cell population dynamics and describe a statistical model that relates CFSE-based flow cytometry data to such models. A parameter estimation scheme is summarized and then applied to a large body of data to assess experimental variability (variation in parameter estimates as identical experiments are replicated) and biological variability (differences in parameter estimates obtained for different donors and cell types) in the context of these models. Variability in the data obtained from replicated experiments is also discussed. The results of this study indicate that many of the cyton model parameters for describing cell proliferation can be reliably estimated using our approach; however, they also show that substantial changes to our mathematical model and/or experimental procedures may be required to ensure identifiability of the remaining cell proliferation parameters.

scholarly journals cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data

2020 ◽  
Vol 24 (11) ◽  
pp. 3173-3181
Author(s):  
Marco S. Nobile ◽  
Eric Nisoli ◽  
Thalia Vlachou ◽  
Simone Spolaor ◽  
Paolo Cazzaniga ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Proliferation ◽  
Flow Cytometry Data

scholarly journals optimalFlow: optimal transport approach to flow cytometry gating and population matching

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Eustasio del Barrio ◽  
Hristo Inouzhe ◽  
Jean-Michel Loubes ◽  
Carlos Matrán ◽  
Agustín Mayo-Íscar
Keyword(s):  
Flow Cytometry ◽  
Supervised Learning ◽  
Optimal Transport ◽  
Cell Types ◽  
R Package ◽  
Supervised Machine Learning ◽  
Intrinsic Variability ◽  
Flow Cytometry Data ◽  
Different Characteristics ◽  
Better Than

Abstract Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. Results We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. Conclusions optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

scholarly journals Circulating Exosomes Inhibit B Cell Proliferation and Activity

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2110
Author(s):  
Jan C. Schroeder ◽  
Lisa Puntigam ◽  
Linda Hofmann ◽  
Sandra S. Jeske ◽  
Inga J. Beccard ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Proliferation ◽  
B Cells ◽  
B Cell ◽  
Surface Expression ◽  
Receptor Expression ◽  
Healthy Individuals ◽  
Inhibitory Effects ◽  
Healthy Donors ◽  
Checkpoint Receptors

(1) Background: Head and neck squamous cell carcinoma (HNSCC) is characterized by a distinctive suppression of the anti-tumor immunity, both locally in the tumor microenvironment (TME) and the periphery. Tumor-derived exosomes mediate this immune suppression by directly suppressing T effector function and by inducing differentiation of regulatory T cells. However, little is known about the effects of exosomes on B cells. (2) Methods: Peripheral B cells from healthy donors and HNSCC patients were isolated and checkpoint receptor expression was analyzed by flow cytometry. Circulating exosomes were isolated from the plasma of HNSCC patients (n = 21) and healthy individuals (n = 10) by mini size-exclusion chromatography. B cells from healthy individuals were co-cultured with isolated exosomes for up to 4 days. Proliferation, viability, surface expression of checkpoint receptors, and intracellular signaling were analyzed in B cells by flow cytometry. (3) Results: Expression of the checkpoint receptors PD-1 and LAG3 was increased on B cells from HNSCC patients. The protein concentration of circulating exosomes was increased in HNSCC patients as compared to healthy donors. Both exosomes from healthy individuals and HNSCC patients inhibited B cell proliferation and survival, in vitro. Surface expression of inhibitory and stimulatory checkpoint receptors on B cells was modulated in co-culture with exosomes. In addition, an inhibitory effect of exosomes on B cell receptor (BCR) signaling was demonstrated in B cells. (4) Conclusions: Plasma-derived exosomes show inhibitory effects on the function of healthy B cells. Interestingly, these inhibitory effects are similar between exosomes from healthy individuals and HNSCC patients, suggesting a physiological B cell inhibitory role of circulating exosomes.

A Novel Automated Analysis Of Flow Cytometry Data Identifies Distinct Cell Signatures In The Immune System Of CLL Patients

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2864-2864
Author(s):  
Jens Rueter ◽  
Vivek Philip ◽  
Krishna Karuturi ◽  
Zaher Oueida ◽  
Margaret Chavaree ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Computational Methods ◽  
Peripheral Blood ◽  
Automated Analysis ◽  
Cell Types ◽  
Analysis Procedure ◽  
Individual Sample ◽  
Flow Cytometry Data ◽  
Distinct Cell ◽  
Common Cell

Abstract Introduction Recent developments of novel immunotherapeutic drugs have shown promising results for patients with hematologic malignancies, however, an unmet need for accurate and specific biomarkers persists. To address this need, we developed a novel integrative analysis procedure for the automated analysis of multidimensional flow cytometry data obtained from the peripheral blood of patients with chronic lymphocytic leukemia (CLL). State of the art flow cytometry analysis is accomplished by manual sequential segmentation, or gating, of cell populations based on similarities in fluorescence and light scatter characteristics through visualization of the data in one- or two-dimensional plots. This approach has a number of limitations, including the subjective nature of the gating and the inability to fully utilize the high-dimensional data. Recent efforts have produced sophisticated computational methods that overcome many of these limitations; however, these newer computational methods have not been rigorously tested in a clinical context and have focused on the rigorous and automated analysis of samples from individual patients, with substantially less effort towards the analysis of patient populations. The ultimate goal of our analysis is to develop computational approaches that will enable an identification of subsets of patients with distinct immunological markers. Methods We developed a novel analysis framework that facilitates automated identification of both common cell types and patient population subgroups, based on post-processing of individual sample analysis with the FLOCK program. FLOCK identifies clusters of putatively similar cells in an individual sample by multidimensional clustering of the fluorescence marker and light-scattering measurements. We developed a rigorous hierarchical clustering approach to identify common “cell signatures” across multiple patients. The cell signatures were then mapped back onto the individual patient samples and used in a second clustering that identified patient subgroups based on similar abundances of specific cell types. Results We used our analytic framework to analyze multidimensional flow cytometry data (26 cell surface markers in 4 different antibody cocktails) from peripheral blood specimens of a heterogeneous group of 55 CLL patients and 13 healthy controls. Our analysis revealed distinct differences between controls and CLL patients. Analyzing the non-malignant peripheral blood cell types, we were furthermore able to differentiate between distinct clinical subpopulations of patients (e.g. identify treatment-naïve patients from those that had previously undergone chemotherapy). Conclusion/Discussion Using a novel integrative analysis procedure to analyze complex flow cytometry data of the peripheral blood from CLL patients, we are able to identify distinct cell type distributions. We propose that this information is a marker for the overall health/disease status of the corresponding patient, and could ultimately be used for diagnosis, prognosis, and selection of optimal treatment. In the context of multiple novel treatment options for CLL patients, such a tool will be crucial for defining individual patient prognosis, and defining an accurately matched treatment plan. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Tumor-Educated Platelets and Angiogenesis in Glioblastoma: Another Brick in the Wall for Novel Prognostic and Targetable Biomarkers, Changing the Vision from a Localized Tumor to a Systemic Pathology

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 294 ◽  
Author(s):  
Rolando Campanella ◽  
Laura Guarnaccia ◽  
Chiara Cordiglieri ◽  
Elena Trombetta ◽  
Manuela Caroli ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Proliferation ◽  
Endothelial Cells ◽  
Protein Content ◽  
Western Blotting ◽  
Thrombin Receptor ◽  
Angiogenic Potential ◽  
Healthy Donors ◽  
Angiogenic Effect ◽  
Systemic Pathology

Circulating platelets (PLTs) are able to affect glioblastoma (GBM) microenvironment by supplying oncopromoter and pro-angiogenic factors. Among these mediators, sphingosine-1-phophate (S1P) has emerged as a potent bioactive lipid enhancing cell proliferation and survival. Here, we investigated the effect of “tumor education”, characterizing PLTs from GBM patients in terms of activation state, protein content, and pro-angiogenic potential. PLTs from healthy donors (HD-PLTs) and GBM patients (GBM-PLTs) were collected, activated, and analyzed by flow cytometry, immunofluorescence, and Western blotting. To assess the pro-angiogenic contribution of GBM-PLTs, a functional cord formation assay was performed on GBM endothelial cells (GECs) with PLT-releasate. GBM-PLTs expressed higher positivity for P-selectin compared to HD-PLTs, both in basal conditions and after stimulation with adenosine triphosphate (ADP) and thrombin receptor activating peptide (TRAP). PLTs showed higher expression of VEGFR-1, VEGFR-2, VWF, S1P, S1PR1, SphK1, and SPNS. Interestingly, increased concentrations of VEGF and its receptors VEGFR1 and VEGFR2, VWF, and S1P were found in GBM-PLT-releasate with respect to HD-PLTs. Finally, GBM-PLT-releasate showed a pro-angiogenic effect on GECs, increasing the vascular network’s complexity. Overall, our results demonstrated the contribution of PLTs to GBM angiogenesis and aggressiveness, advancing the potential of an anti-PLT therapy and the usefulness of PLT cargo as predictive and monitoring biomarkers.

scholarly journals Gating mass cytometry data by deep learning

2016 ◽  
Author(s):  
Huamin Li ◽  
Uri Shaham ◽  
Kelly P. Stanton ◽  
Yi Yao ◽  
Ruth Montgomery ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Blood Cells ◽  
Single Cell Analysis ◽  
Domain Adaptation ◽  
Cell Types ◽  
Source Distribution ◽  
Mass Cytometry ◽  
Flow Cytometry Data ◽  
History Of

AbstractMass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that Deep-CyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured accross several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i)14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data.

scholarly journals Quantifying rates of cell migration and cell proliferation in co-culture barrier assays reveals how skin and melanoma cells interact during melanoma spreading and invasion

2017 ◽  
Author(s):  
Parvathi Haridas ◽  
Catherine J. Penington ◽  
Jacqui A. McGovern ◽  
D. L. Sean McElwain ◽  
Matthew J. Simpson
Keyword(s):  
Cell Proliferation ◽  
Spatial Arrangement ◽  
Cell Types ◽  
Melanoma Cells ◽  
Heterogeneous Population ◽  
Cell Contact ◽  
Parameter Estimates ◽  
Fibroblast Cells

ABSTRACTMalignant spreading involves the migration of cancer cells amongst other native cell types. For example, in vivo melanoma invasion involves individual melanoma cells migrating through native skin, which is composed of several distinct subpopulations of cells. Here, we aim to quantify how interactions between melanoma and fibroblast cells affect the collective spreading of a heterogeneous population of these cells in vitro. We perform a suite of circular barrier assays that includes: (i) monoculture assays with fibroblast cells; (ii) monoculture assays with SK-MEL-28 melanoma cells; and (iii) a series of co-culture assays initiated with three different ratios of SK-MEL-28 melanoma cells and fibroblast cells. Using immunostaining, detailed cell density histograms are constructed to illustrate how the two subpopulations of cells are spatially arranged within the spreading heterogeneous population. Calibrating the solution of a continuum partial differential equation to the experimental results from the monoculture assays allows us to estimate the cell diffusivity and the cell proliferation rate for the melanoma and the fibroblast cells, separately. Using the parameter estimates from the monoculture assays, we then make a prediction of the spatial spreading in the co-culture assays. Results show that the parameter estimates obtained from the monoculture assays lead to a reasonably accurate prediction of the spatial arrangement of the two subpopulations in the co-culture assays. Overall, the spatial pattern of spreading of the melanoma cells and the fibroblast cells is very similar in monoculture and co-culture conditions. Therefore, we find no clear evidence of any interactions other than cell-to-cell contact and crowding effects.

scholarly journals The MAL Protein, an Integral Component of Specialized Membranes, in Normal Cells and Cancer

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1065
Author(s):  
Armando Rubio-Ramos ◽  
Leticia Labat-de-Hoz ◽  
Isabel Correas ◽  
Miguel A. Alonso
Keyword(s):  
Epithelial Cells ◽  
Large Body ◽  
Original Description ◽  
Cell Types ◽  
Future Research ◽  
Transmembrane Segments ◽  
Epsilon Toxin ◽  
Proteolipid Proteins ◽  
Polarized Epithelial Cells

The MAL gene encodes a 17-kDa protein containing four putative transmembrane segments whose expression is restricted to human T cells, polarized epithelial cells and myelin-forming cells. The MAL protein has two unusual biochemical features. First, it has lipid-like properties that qualify it as a member of the group of proteolipid proteins. Second, it partitions selectively into detergent-insoluble membranes, which are known to be enriched in condensed cell membranes, consistent with MAL being distributed in highly ordered membranes in the cell. Since its original description more than thirty years ago, a large body of evidence has accumulated supporting a role of MAL in specialized membranes in all the cell types in which it is expressed. Here, we review the structure, expression and biochemical characteristics of MAL, and discuss the association of MAL with raft membranes and the function of MAL in polarized epithelial cells, T lymphocytes, and myelin-forming cells. The evidence that MAL is a putative receptor of the epsilon toxin of Clostridium perfringens, the expression of MAL in lymphomas, the hypermethylation of the MAL gene and subsequent loss of MAL expression in carcinomas are also presented. We propose a model of MAL as the organizer of specialized condensed membranes to make them functional, discuss the role of MAL as a tumor suppressor in carcinomas, consider its potential use as a cancer biomarker, and summarize the directions for future research.

scholarly journals Extracellular-Vesicle-Based Coatings Enhance Bioactivity of Titanium Implants—SurfEV

Nanomaterials ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1445
Author(s):  
Taisa Nogueira Pansani ◽  
Thanh Huyen Phan ◽  
Qingyu Lei ◽  
Alexey Kondyurin ◽  
Bill Kalionis ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Titanium Surface ◽  
Wound Closure ◽  
Cell Types ◽  
Extracellular Vesicle ◽  
Tissue Growth ◽  
Titanium Implants ◽  
The Body ◽  
High Concentrations ◽  
And Migration

Extracellular vesicles (EVs) are nanoparticles released by cells that contain a multitude of biomolecules, which act synergistically to signal multiple cell types. EVs are ideal candidates for promoting tissue growth and regeneration. The tissue regenerative potential of EVs raises the tantalizing possibility that immobilizing EVs on implant surfaces could potentially generate highly bioactive and cell-instructive surfaces that would enhance implant integration into the body. Such surfaces could address a critical limitation of current implants, which do not promote bone tissue formation or bond bone. Here, we developed bioactive titanium surface coatings (SurfEV) using two types of EVs: secreted by decidual mesenchymal stem cells (DEVs) and isolated from fermented papaya fluid (PEVs). For each EV type, we determined the size, morphology, and molecular composition. High concentrations of DEVs enhanced cell proliferation, wound closure, and migration distance of osteoblasts. In contrast, the cell proliferation and wound closure decreased with increasing concentration of PEVs. DEVs enhanced Ca/P deposition on the titanium surface, which suggests improvement in bone bonding ability of the implant (i.e., osteointegration). EVs also increased production of Ca and P by osteoblasts and promoted the deposition of mineral phase, which suggests EVs play key roles in cell mineralization. We also found that DEVs stimulated the secretion of secondary EVs observed by the presence of protruding structures on the cell membrane. We concluded that, by functionalizing implant surfaces with specialized EVs, we will be able to enhance implant osteointegration by improving hydroxyapatite formation directly at the surface and potentially circumvent aseptic loosening of implants.

scholarly journals Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 387
Author(s):  
Yiting Liang ◽  
Yuanhua Zhang ◽  
Yonggang Li
Keyword(s):  
Parameter Estimation ◽  
Kinetic Model ◽  
Model Parameters ◽  
Hydrogen Ions ◽  
Removal Process ◽  
Cobalt Removal ◽  
Estimation Scheme ◽  
Zinc Hydrometallurgy ◽  
The Impact ◽  
Parameter Estimation Scheme

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

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