scholarly journals Dynamic changes in single cell and population activity during the acquisition of task behavior

2010 ◽  
Vol 4 ◽  
Author(s):  
Buhusi Catalin
Keyword(s):  
Single Cell ◽  
Dynamic Changes ◽  
Population Activity ◽  
Task Behavior

Reaction Time and Time Course of Neuronal Responses in MT and MST at Different Stimulus Contrasts

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 157-157 ◽  
Author(s):  
A Thiele ◽  
K-P Hoffmann
Keyword(s):  
Reaction Time ◽  
Single Cell ◽  
Single Cells ◽  
Direction Selectivity ◽  
Contrast Level ◽  
Population Activity ◽  
Temporal Area ◽  
Population Mean ◽  
The Mean ◽  
Mean Reaction Time

Direction-selective neurons from the middle temporal area (MT) and the middle superior temporal area (MST) were recorded while a monkey performed a direction discrimination task. Stimuli consisted of evenly spaced bars moving in one of the four cardinal directions. Monkey's reaction time, single-cell latency, and direction selectivity were calculated when stimuli of 53%, 24%, and 4% contrast were presented, and the monkey indicated a correct decision. Mean reaction time was 359±77 ms at 53% contrast, 391±107 ms at 24% contrast, and 582±374 ms at 4% contrast. Most neurons exhibiting direction selective responses at 53% contrast was also active at 24% contrast (MT, 99%; MST, 88%). The number of neurons still exhibiting stimulus-related activity at 4% contrast dramatically decreased (MT to 28%; MST to 41%). Shortest latencies were found at high contrast level (53% contrast; MT, 29 ms; population mean, 76±40 ms; MST, 35 ms; population mean, 77±27 ms). Single cell and population latency increased at lower contrast (4% contrast: MT minimum, 86 ms; population mean, 180±76 ms; MST minimum, 97 ms; population mean, 205±56 ms). This indicates that the mean increase in latency at the single-cell level only partially reflects the increase in reaction time (mean reaction time increased by 223 ms, while mean single-cell latency increased by ∼100 ms in MT and MST). We therefore calculated the normalised population response at different contrast levels. The maximal population activity was always found at the highest contrast level and this was set to 1. In MT it took 75 – 80 ms from stimulus onset until half maximal activity (0.5) was reached at 53% contrast. To reach 0.5 took 85 – 90 ms at 24% contrast and 205 – 210 ms at 4% contrast. For MST the respective values were 85 ms (53% contrast), 90 ms (24% contrast) and 255 ms (4%) contrast. Thus the time to reach half the maximal population activity much better reflects the reaction time than the mean of the latencies calculated from single cells.

scholarly journals Imaging of respiratory-related population activity with single-cell resolution

2007 ◽  
Vol 292 (1) ◽  
pp. C508-C516 ◽  
Author(s):  
Frank Funke ◽  
Mathias Dutschmann ◽  
Michael Müller
Keyword(s):  
Single Cell ◽  
Neuronal Activity ◽  
Single Cells ◽  
Activity Patterns ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Respiratory Neurons ◽  
Ventrolateral Medulla ◽  
Population Activity ◽  
Respiratory Network

The pre-Bötzinger complex (PBC) in the rostral ventrolateral medulla contains a kernel involved in respiratory rhythm generation. So far, its respiratory activity has been analyzed predominantly by electrophysiological approaches. Recent advances in fluorescence imaging now allow for the visualization of neuronal population activity in rhythmogenic networks. In the respiratory network, voltage-sensitive dyes have been used mainly, so far, but their low sensitivity prevents an analysis of activity patterns of single neurons during rhythmogenesis. We now have succeeded in using more sensitive Ca2+ imaging to study respiratory neurons in rhythmically active brain stem slices of neonatal rats. For the visualization of neuronal activity, fluo-3 was suited best in terms of neuronal specificity, minimized background fluorescence, and response magnitude. The tissue penetration of fluo-3 was improved by hyperosmolar treatment (100 mM mannitol) during dye loading. Rhythmic population activity was imaged with single-cell resolution using a sensitive charge-coupled device camera and a ×20 objective, and it was correlated with extracellularly recorded mass activity of the contralateral PBC. Correlated optical neuronal activity was obvious online in 29% of slices. Rhythmic neurons located deeper became detectable during offline image processing. Based on their activity patterns, 74% of rhythmic neurons were classified as inspiratory and 26% as expiratory neurons. Our approach is well suited to visualize and correlate the activity of several single cells with respiratory network activity. We demonstrate that neuronal synchronization and possibly even network configurations can be analyzed in a noninvasive approach with single-cell resolution and at frame rates currently not reached by most scanning-based imaging techniques.

scholarly journals An improved ATP FRET sensor for yeast shows heterogeneity during nutrient transitions

2019 ◽  
Author(s):  
Dennis Botman ◽  
Johan H. van Heerden ◽  
Bas Teusink
Keyword(s):  
Single Cell ◽  
Dynamic Environments ◽  
Dynamic Responses ◽  
Yeast Cells ◽  
Single Cell Level ◽  
Wild Type ◽  
Cell Level ◽  
Dynamic Changes ◽  
Atp Levels ◽  
Small Subpopulation

AbstractAdenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at the single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and show that a small subpopulation of wild type cells reached an imbalanced state during glycolytic startup, characterised by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments.

scholarly journals Enhancing single-cell cellular state inference by incorporating molecular network features

2019 ◽  
Author(s):  
Ji Dong ◽  
Peijie Zhou ◽  
Yichong Wu ◽  
Wendong Wang ◽  
Yidong Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Goblet Cells ◽  
Summary Score ◽  
Molecular Network ◽  
Molecular Networks ◽  
Gene Interactions ◽  
Rna Seq ◽  
Dynamic Changes ◽  
State Inference ◽  
Microfold Cells

AbstractIn biological systems, genes function in conjunction rather than in isolation. However, traditional single-cell RNA-seq (scRNA-seq) analyses heavily rely on the transcriptional similarity of individual genes, ignoring the inherent gene-gene interactions. Here, we present SCORE, a network-based method, which incorporates the validated molecular network features to infer cellular states. Using real scRNA-seq datasets, SCORE outperforms existing methods in accuracy, robustness, scalability, data integration and removal of batch effect. When applying SCORE to a newly generated human ileal scRNA-seq dataset, we identified several novel stem/progenitor clusters, including a Cripto-1+ cluster. Moreover, two distinct groups of goblet cells were identified and only one of them tended to secrete mucus. Besides, we found that the recently identified BEST4+OTOP2+ microfold cells also highly expressed CFTR, which is different from their colonic counterparts. In summary, SCORE enhances cellular state inference by simulating the dynamic changes of molecular networks, providing more biological insights beyond statistical interpretations.

scholarly journals Single-Cell Reconstruction of Emerging Population Activity in an Entire Developing Circuit

Cell ◽  
2019 ◽  
Vol 179 (2) ◽  
pp. 355-372.e23 ◽  
Author(s):  
Yinan Wan ◽  
Ziqiang Wei ◽  
Loren L. Looger ◽  
Minoru Koyama ◽  
Shaul Druckmann ◽  
...  
Keyword(s):  
Single Cell ◽  
Population Activity

scholarly journals Population activity statistics dissect subthreshold and spiking variability in V1

2017 ◽  
Vol 118 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Mihály Bányai ◽  
Zsombor Koman ◽  
Gergő Orbán
Keyword(s):  
Membrane Potential ◽  
Single Cell ◽  
Prediction Models ◽  
Stimulus Change ◽  
Population Level ◽  
Response Variability ◽  
Population Activity ◽  
Information Encoding ◽  
Model Predictions ◽  
Neural Variability

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity.

scholarly journals Single cell phospho-specific flow cytometry can detect dynamic changes of phospho-Stat1 level in lung cancer cells

2010 ◽  
Vol 77A (11) ◽  
pp. 1008-1019 ◽  
Author(s):  
Chien-Chung Lin ◽  
Wei-Lun Huang ◽  
Wen-Pin Su ◽  
Helen H. W. Chen ◽  
Wu-Wei Lai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Flow Cytometry ◽  
Single Cell ◽  
Cancer Cells ◽  
Lung Cancer Cells ◽  
Dynamic Changes ◽  
Specific Flow

scholarly journals Dynamic changes in the regulatory T-cell heterogeneity and function by murine IL-2 mutein

2020 ◽  
Vol 3 (5) ◽  
pp. e201900520 ◽  
Author(s):  
Daniel R Lu ◽  
Hao Wu ◽  
Ian Driver ◽  
Sarah Ingersoll ◽  
Sue Sohn ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Control Cell ◽  
Mutant Form ◽  
Rna Seq ◽  
Dynamic Changes ◽  
Cell Clustering ◽  
Transcriptional Changes ◽  
And Function

The therapeutic expansion of Foxp3+ regulatory T cells (Tregs) shows promise for treating autoimmune and inflammatory disorders. Yet, how this treatment affects the heterogeneity and function of Tregs is not clear. Using single-cell RNA-seq analysis, we characterized 31,908 Tregs from the mice treated with a half-life extended mutant form of murine IL-2 (IL-2 mutein, IL-2M) that preferentially expanded Tregs, or mouse IgG Fc as a control. Cell clustering analysis revealed that IL-2M specifically expands multiple sub-states of Tregs with distinct expression profiles. TCR profiling with single-cell analysis uncovered Treg migration across tissues and transcriptional changes between clonally related Tregs after IL-2M treatment. Finally, we identified IL-2M–expanded Tnfrsf9+Il1rl1+ Tregs with superior suppressive function, highlighting the potential of IL-2M to expand highly suppressive Foxp3+ Tregs.

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