scholarly journals Multiplexed, sequential secretion analysis of the same single cells reveals distinct effector response dynamics dependent on the initial basal state

2019 ◽  
Author(s):  
Zhuo Chen ◽  
Yao Lu ◽  
Kerou Zhang ◽  
Yang Xiao ◽  
Jun Lu ◽  
...  
Keyword(s):  
Immune Cells ◽  
Time Course ◽  
Single Cells ◽  
Human Macrophage ◽  
Transcriptional Level ◽  
Multiple Time ◽  
Response Dynamics ◽  
Macrophage Response ◽  
Time Points ◽  
Effector Response

AbstractThe effector response of immune cells dictated by an array of secreted proteins is a highly dynamic process, requiring sequential measurement of all relevant proteins from single cells. Herein we show a microchip-based, 10-plexed, sequential secretion assay on the same single cells and at the scale of ~5000 single cells measured simultaneously over 4 time points. It was applied to investigating the time course of single human macrophage response to Toll-like receptor 4 (TLR4) ligand lipopolysaccharide and revealed four distinct activation modes for different proteins in single cells. In particular, we observed that secreted factors regulated by transcription factor NFkB (e.g., TNF and CCL2) predominantly show on-off mode over off-on mode. The dynamics of all proteins combined classified the cells into two major activation states, which were found to be dependent on the basal state of each cell. Single-cell RNA-Seq was performed on the same samples at the matched time points and further demonstrated at the transcriptional level the existence of two major activation states, which are enriched for translation vs inflammatory programs, respectively. These results showed a cell-intrinsic heterogeneous response in phenotypically homogeneous cell population. This work demonstrated the longitudinal tracking of protein secretion signature in thousands of single cells at multiple time points, providing dynamic information to better understand how individual immune cells react to pathogenic challenges over time and how they together constitute a population response.

scholarly journals Temporal gene expression in bovine corpora lutea after treatment with PGF2alpha based on serial biopsies in vivo

Reproduction ◽  
2001 ◽  
pp. 905-913 ◽  
Author(s):  
SJ Tsai ◽  
K Kot ◽  
OJ Ginther ◽  
MC Wiltbank
Keyword(s):  
Gene Expression ◽  
Corpus Luteum ◽  
Time Course ◽  
Regulatory Protein ◽  
Corpora Lutea ◽  
Multiple Time ◽  
Lh Receptor ◽  
Time Points ◽  
Multiple Biopsies ◽  
Fp Receptor

There is growing evidence to indicate that PGF(2alpha)-induced luteolysis involves altered gene expression in the corpus luteum. Concentrations of mRNA encoding nine different gene products were quantified at three time points from corpora lutea in situ. Serial luteal biopsies (2.1-5.5 mg per biopsy) were collected using an ultrasound-guided transvaginal method and mRNA concentrations were quantified with standard curve quantitative competitive RT-PCR. In the first experiment, three luteal biopsies were collected from three heifers and analysed in multiple assays to evaluate the repeatability of the methods. Concentrations of mRNA for glyceraldehyde 3-phosphate dehydrogenase (GAPDH), PGF(2alpha) receptor (FP receptor) and LH receptor were found to be highly repeatable between assays, between multiple biopsies and between animals (coefficients of variation 1.3-17.3%). In the second experiment, heifers on days 9-11 after ovulation were assigned randomly to receive saline only (n = 6), saline with biopsies taken at t = 0, 0.5 and 4.0 h after injection (n = 6), PGF(2alpha) only (n = 6) or PGF(2alpha) with biopsies taken at t = 0, 0.5 and 4.0 h after treatment (n = 7). Biopsy alone did not change corpus luteum diameter, serum progesterone concentrations or days to next ovulation within the saline- or PGF(2alpha)-treated groups. Concentrations of mRNA for steroidogenic acute regulatory protein, FP receptor, 3beta-hydroxysteroid dehydrogenase, cytosolic phospholipase A(2) and LH receptor were decreased at 4.0 h after PGF(2alpha) injection. In contrast, PGF(2alpha) increased mRNA concentrations for prostaglandin G/H synthase-2, monocyte chemoattractant protein-1 and c-fos but the time course differed for induction of these mRNAs. Concentrations of mRNA for GAPDH did not change after PGF(2alpha) treatment. In conclusion, the techniques allowed analysis of multiple, specific mRNAs in an individual corpus luteum at multiple time points without altering subsequent luteal function. Use of these techniques confirmed that luteolysis involves both up- and downregulation of specific mRNA by PGF(2alpha).

scholarly journals Multiplexed, Sequential Secretion Analysis of the Same Single Cells Reveals Distinct Effector Response Dynamics Dependent on the Initial Basal State

2019 ◽  
Vol 6 (9) ◽  
pp. 1801361 ◽  
Author(s):  
Zhuo Chen ◽  
Yao Lu ◽  
Kerou Zhang ◽  
Yang Xiao ◽  
Jun Lu ◽  
...  
Keyword(s):  
Single Cells ◽  
Response Dynamics ◽  
Effector Response

scholarly journals Longitudinal cancer evolution from single cells

2020 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Fabrizio Angaroni ◽  
Davide Maspero ◽  
Gianluca Ascolani ◽  
Isabella Castiglioni ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Clonal Evolution ◽  
Multiple Time ◽  
Cancer Evolution ◽  
Weighted Likelihood ◽  
Time Points ◽  
Single Cell Sequencing ◽  
The Impact

ABSTRACTThe rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones.We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data.Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level.To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy. Furthermore, the analysis of a longitudinal dataset of breast cancer PDXs from targeted scDNA-sequencing experiments delivers a high-resolution characterization of intra-tumor heterogeneity, also allowing the detection of a late de novo subclone.

scholarly journals Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

2017 ◽  
Author(s):  
Mariana Gómez-Schiavon ◽  
Liang-Fu Chen ◽  
Anne E. West ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Posterior Probability ◽  
Single Cells ◽  
Bayesian Posterior Probability ◽  
Model Parameters ◽  
Multiple Time ◽  
Stochastic Gene Expression ◽  
Time Points ◽  
Multiple Time Points

AbstractSingle-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.Author SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters (θ) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P(θ|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

scholarly journals Characterization of directed differentiation by high-throughput single-cell RNA-Seq

2014 ◽  
Author(s):  
Magali Soumillon ◽  
Davide Cacchiarelli ◽  
Stefan Semrau ◽  
Alexander van Oudenaarden ◽  
Tarjei S Mikkelsen
Keyword(s):  
Gene Expression ◽  
Disease Modeling ◽  
Single Cells ◽  
Adipogenic Differentiation ◽  
Digital Gene Expression ◽  
Multiple Time ◽  
Directed Differentiation ◽  
Time Points

Directed differentiation of cells in vitro is a powerful approach for dissection of developmental pathways, disease modeling and regenerative medicine, but analysis of such systems is complicated by heterogeneous and asynchronous cellular responses to differentiation-inducing stimuli. To enable deep characterization of heterogeneous cell populations, we developed an efficient digital gene expression profiling protocol that enables surveying of mRNA in thousands of single cells at a time. We then applied this protocol to profile 12,832 cells collected at multiple time points during directed adipogenic differentiation of human adipose-derived stem/stromal cells in vitro. The resulting data reveal the major axes of cell-to-cell variation within and between time points, and an inverse relationship between inflammatory gene expression and lipid accumulation across cells from a single donor.

scholarly journals Sustained Increases in Immune Transcripts and Immune Cell Trafficking During the Recovery of Experimental Brain Ischemia

Stroke ◽  
2020 ◽  
Vol 51 (8) ◽  
pp. 2514-2525 ◽  
Author(s):  
Wen Fury ◽  
Keun Woo Park ◽  
Zhuhao Wu ◽  
Eunhee Kim ◽  
Moon-sook Woo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Immune Cells ◽  
Fluorescent Protein ◽  
Immune Cell ◽  
Chronic Phase ◽  
Mononuclear Phagocytes ◽  
Multiple Time ◽  
Immune Gene ◽  
Cell Trafficking ◽  
Time Points

Background and Purpose: Stroke is a major cause of chronic neurological disability. There is considerable interest in understanding how acute transcriptome changes evolve into subacute and chronic patterns that facilitate or limit spontaneous recovery. Here we mapped longitudinal changes in gene expression at multiple time points after stroke in mice out to 6 months. Methods: Adult C57BL/6 mice were subjected to transient middle cerebral artery occlusion. Longitudinal transcriptome levels were measured at 10 time points after stroke from acute to recovery phases of ischemic stroke. Localization and the number of mononuclear phagocytes were determined in the postischemic brain. Whole-mount brain imaging was performed in asplenic mice receiving GFP + (green fluorescent protein)-tagged splenocytes. Results: Sustained stroke-induced mRNA abundance changes were observed in both hemispheres with 2989 ipsilateral and 822 contralateral genes significantly perturbed. In the hemisphere ipsilateral to the infarct, genes associated with immune functions were strongly affected, including temporally overlapping innate and adaptive immunity and macrophage M1 and M2 phenotype-related genes. The strong immune gene activation was accompanied by the sustained infiltration of peripheral immune cells at acute, subacute, and recovery stages of stroke. The infiltrated immune cells were found in the infarcted area but also in remote regions at 2 months after stroke. Conclusions: The study identifies that immune components are the predominant molecular signatures and they may propagate or continuously respond to brain injury in the subacute to chronic phase after central nervous system injury. The study suggests a potential immune-based strategy to modify injury progression and tissue remodeling in ischemic stroke, even months after the initiating event.

Time Course of Simulator Sickness in Asian Military Pilots

2020 ◽  
Vol 91 (11) ◽  
pp. 892-896
Author(s):  
Janine En Qi Loi ◽  
Magdalene Li Ling Lee ◽  
Benjamin Boon Chuan Tan ◽  
Brian See
Keyword(s):  
Air Force ◽  
Time Course ◽  
Simulator Training ◽  
Simulator Sickness ◽  
Time Points ◽  
True Prevalence ◽  
Subjective Scale ◽  
Significant Difference ◽  
Simulator Sickness Questionnaire ◽  
Military Pilots

INTRODUCTION: This study sought to determine the incidence, severity, and time-course of simulator sickness (SS) among Asian military pilots following flight simulator training.METHODS: A survey was conducted on Republic of Singapore Air Force pilots undergoing simulator training. Each subject completed a questionnaire immediately after (0H), and at the 3-h (3H) and 6-h (6H) marks. The questionnaire included the simulator sickness questionnaire (SSQ) and a subjective scale to rate their confidence to fly.RESULTS: In this study, 258 pilots with a median age of 31.50 yr (range, 2155 yr) and mean age of 32.61 6.56 yr participated. The prevalence of SS was 48.1% at 0H, 30.8% at 3H, and 16.4% at 6H. Based on a threshold of an SSQ score >10, the prevalence of operationally significant SS was 33.3% at 0H, 13.2% at 3H, and 8.1% at 6H. The most frequent symptoms were fatigue (38.1%), eye strain (29.0%), and fullness of head (19.9%). There was no significant difference in mean scores between rotary and fixed wing pilots. Older, more experienced pilots had greater scores at 0H, but this association did not persist. A correlation was found between SSQ score and self-reported confidence.DISCUSSION: To our knowledge, this study is the first to report the prevalence of operationally significant SS in Asian military pilots over serial time points. Most pilots with SS are able to subjectively judge their fitness to fly. Sensitivity analysis suggests the true prevalence of SS symptoms at 3H and 6H to be closer to 23.8% and 12.0%, respectively.Loi JEQ, Lee MLL, Tan BBC, See B. Time course of simulator sickness in Asian military pilots. Aerosp Med Hum Perform. 2020; 91(11):892896.

scholarly journals Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism

2021 ◽  
Vol 5 (1) ◽  
pp. e000700
Author(s):  
Carrie Allison ◽  
Fiona E Matthews ◽  
Liliana Ruta ◽  
Greg Pasco ◽  
Renee Soufer ◽  
...  
Keyword(s):  
Phase 1 ◽  
Autism Spectrum ◽  
Population Screening ◽  
Diagnostic Assessment ◽  
Test Accuracy ◽  
Multiple Time ◽  
Phase 2 ◽  
Time Points ◽  
Screening Study

ObjectiveThis is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.DesignObservational study.SettingLuton, Bedfordshire and Cambridgeshire in the UK.Participants13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18–30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4.InterventionsA stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT.Main outcome measuresConsensus diagnostic outcome at phase 1 and phase 2.ResultsAt phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2.ConclusionsThe Q-CHAT can be used at 18–30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4–5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period.

scholarly journals Serial computed tomography findings of Coronavirus disease 2019 (COVID-19) pneumonia treated with favipiravir and steroid therapy: report of 11 cases

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Naoki Irizato ◽  
Hiroshi Matsuura ◽  
Atsuya Okada ◽  
Ken Ueda ◽  
Hitoshi Yamamura
Keyword(s):  
Computed Tomography ◽  
Mechanical Ventilation ◽  
Steroid Therapy ◽  
Time Course ◽  
Ground Glass ◽  
Time Points ◽  
Ct Findings ◽  
Mixed Pattern ◽  
Bronchial Dilatation

Abstract Background This study evaluated the time course of computed tomography (CT) findings of patients with COVID-19 pneumonia who required mechanical ventilation and were treated with favipiravir and steroid therapy. Results Eleven patients with severe COVID-19 pneumonia were included. CT findings assessed at the three time points showed that all patients had ground-glass opacities (GGO) and consolidation and mixed pattern at intubation. Consolidation and mixed pattern disappeared in most of the patients whereas GGO persisted in all patients at 1-month follow-up. In addition to GGO, a subpleural line and bronchus distortion and bronchial dilatation were frequent findings. The degree of resolution of GGO varied depending on each patient. The GGO score correlated significantly with the time from symptoms onset to initiation of steroid therapy (ρ = 0.707, p = 0.015). Conclusions At 1-month follow-up after discharge, non-GGO lesions were absorbed almost completely, and GGO were a predominant CT manifestation. Starting steroid therapy earlier after onset of symptoms in severe COVID-19 pneumonia may reduce the extent of GGO at 1-month follow-up.

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