Biology subjects

Billings, J.

Publications and source records attributed to Billings, J..

6 recordsLinked to original sources

bioRxiv2022

Fisetin Attenuates Cellular Senescence Accumulation During Culture Expansion of Human Adipose-Derived Stem Cells

Mesenchymal stem cells (MSCs) have long been viewed as a promising therapeutic for musculoskeletal repair. However, regulatory concerns including tumorgenicity, inconsistencies in preparation techniques, donor-to-donor variability, and the accumulation of senescence during culture expansion have hindered the clinical application of MSCs. Senescence, in particular, is a driving mechanism for MSC dysfunction with advancing age. Often characterized by increased reactive oxygen species, senescence-associated heterochromatin foci, inflammatory cytokine and chemokine secretion, and reduced proliferative capacity, senescence directly inhibits MSCs efficacy as a therapeutic for musculoskeletal regeneration and repair. Furthermore, autologous delivery of senescent MSCs can further induce disease and aging progression through the secretion of the senescence-associated secretory phenotype (SASP) and mitigate the regenerative potentetial of MSCs. To combat these issues, the use of senolytic agents to selectively clear senescent cell populations has become popular. However, their benefits to human MSCs during the culture expansion process have not yet been elucidated. To address this, analyzed markers of senescence during culturing of human primary adipose-derived stem cells (ADSCs), a population of fat-resident MSCs commonly used in regenerative medicine applications. Next, we used the senolytic agent fisetin to determine if we can reduce these markers of senescence within our culture-expanded ADSC populations. Our results indicate that ADSCs acquire common markers of cellular senescence including increased reactive oxygen species, senescence-associated {beta}-galactosidase, and senescence-associated heterochromatin foci. Furthermore, we found that the senolytic agent fisetin works in a dose-dependent manner and selectively attenuates these markers of senescence while maintaining the differentiation potential of the expanded ADSCs. Signficance StatementThe accumulation of dysfunctional, senescent cells throughout aging is not confined to specific tissues and cell types, but instead effects the whole body, including stem cells. Similarly, during culture expansion stem cells accumulate senescence while concurrently losing their regenerative potential. In this study, we found that fisetin (a well known senotherapeutic agent) can reduce the number of senescent cells during stem cell expansion. The current results indicate that fisetin may be used not only as a promising therapeutic to remove senescent cells in stem cell isolates from older individuals but also to reduce the accumulation of senescence during culture expansion.

cell biology↗

bioRxiv2020

Topological Features of Electroencephalography are Reference-Invariant

Electroencephalography (EEG) is among the most widely diffused, inexpensive, and applied neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analysis. It is therefore valuable to obtain quantities that are reference-independent and minimally affected by pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, were neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individuals brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional - topological - level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.

neuroscience↗

bioRxiv2020

Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

Whereas brain imaging tools like functional Magnetic Resonance Imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the datas apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, i.e, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective considers the condition-dependency of the brains Functional Connectivity (FC), i.e., the similarity matrix computed across the variables of a multivariate signal. More recently, metric spaces that think of the data topologically, e.g., as an abstract geometric object, have become available. In the abstract, uncertainty prevails regarding the distortions imposed by the mode of measurement upon the object under study. Features that are invariant under continuous deformations, such as rotation and inflation, constitute the features of topological data analysis. While there are strengths and weaknesses of each metric space, we find that metric spaces that track topological features are optimal descriptors of the brains experimentally defined states. AUTHOR SUMMARYTime-Varying Functional Connectivity (TVFC) leverages brain imaging data to interpret brain function as time-varying patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brains functional self-organization. One tool from TDA, "persistent homology", observes the occurrence and persistence of n-dimensional holes in a sequence of simplicial complexes extracted from a weighted graph. The occurrence of such holes within the TVFC graph may indicate preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional metrics at the task of segmenting brain states that differ across experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment high versus low performance levels under common stimuli, and generalize better across volunteers. These findings support the topological interpretation of brain dynamics.

neuroscience↗

bioRxiv2019

Resting brain fluctuations are intrinsically coupled to visual response dynamics

How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional (f)MRI to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) govern mouse resting brain dynamics. QPPs captured the temporal alignment of global brain fluctuations, anti-correlation of the Default Mode (DMN)- and Task Positive (TPN)-like networks, and activity in neuromodulatory nuclei of the reticular formation. While visual stimulation could trigger a transient spatiotemporal pattern highly similar to intrinsic QPPs, global signal fluctuations and QPPs during rest periods could explain variance in the following visual responses. QPPs and the global signal thus appeared to capture a common arousal-related brain-state fluctuation, orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

neuroscience↗

bioRxiv2018

Quasi-periodic patterns contribute to functional connectivity in the brain

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.\n\nHighlightsO_LIQuasi-periodic patterns (QPPs) of low-frequency activity contribute to functional connectivity\nC_LIO_LIThe spatiotemporal pattern of QPPs differs between resting-state and task-performing individuals\nC_LIO_LIQPPs account for significant functional connectivity in the DMN and TPN during rest and task performance\nC_LIO_LIChanges in functional connectivity in these networks may reflect differences in QPPs\nC_LI

neuroscience↗

bioRxiv2017

Instantaneous Brain Dynamics Mapped to a Continuous State Space

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brains dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brains surface reveals the patterns of brain activity that support each experimental state.

neuroscience↗