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Kauppila, M.

Publications and source records attributed to Kauppila, M..

4 recordsLinked to original sources

Viscoelasticity driven deformation dynamics of the substrate affect mechanically induced Ca2+ signals

The viscoelastic properties of the substrate are known to affect many aspects of cellular behavior from migration to differentiation. Cells are also known to sense deformations in the extracellular matrix (ECM). However, these mechanotransduction events have mostly been studied separately, and the emphasis has been on purely elastic materials although most tissues in the body are viscoelastic. We wanted to understand how the viscosity and elasticity of the substrate affect cells mechanosensation, i.e., how cells integrate passive (substrate viscoelasticity) and active cues (dynamic substrate deformation) into calcium transients. We used a light-controllable Disperse Red 1-glass (DR1-glass) coating together with a thin polyacrylamide (PAA) hydrogel with controllable viscoelasticity. This allowed us to generate local deformations in the cell-substrate interface on substrates with different viscoelastic properties. Inspecting the resulting calcium transients in a Madin Darby canine kidney type II (MDCK II) monolayer with the genetically encoded calcium indicator jRCaMP1b revealed that cells responded to deformation differently depending on the viscoelasticity of the substrate. On stiff elastic gels cells exhibited all in all the largest calcium responses as well as increased sensitivity to the magnitude of deformation, with larger deformations leading to stronger calcium signals, whereas on soft elastic and soft viscoelastic gels the magnitudes of deformation had less effect on the degree of calcium signals. Indeed, immunostainings showed that cells formed the strongest focal adhesions (FA) on stiff gels, albeit differences were surprisingly small. Instead, computational modeling revealed that forces generated at FAs were strongly dependent on the viscoelasticity of the substrate, with increased elasticity and decreased viscosity leading to larger forces. Moreover, viscoelasticity affected the dynamics of the force generation. On stiff elastic gels force increased in fast steps, whereas on soft gels the buildup was gradual and was further slowed down by increased viscosity. Surprisingly, experiments with PIEZO1 KO cells showed that the calcium responses to the substrate deformation did not require PIEZO1 channels. Instead, depleting the ER with thapsigargin (TG), depolymerizing the actin cytoskeleton with cytochalasin D (cyto D) and latrunculin A (Lat A), and inhibiting actomyosin II with y-27632, showed that the calcium was mainly released from the ER in an actin, but not actomyosin, dependent mechanism. The data therefore suggests that the forces subjected to FAs could be directly transmitted to the ER via an actin mediated tension that results in the opening of ER residing calcium release channels. Our results illustrate that the viscoelasticity of the cell niche controls cell behavior in two ways. Firstly, it affects how cells build adhesions to the ECM and thus the accumulation of mechanosensitive proteins. Secondly, it affects the dynamics of the deformations and forces that are sensed by FAs. In line with our previous findings, we show that the mechanically induced calcium responses are dependent on dynamics, with fast mechanical cues resulting in larger calcium responses. Moreover, our research suggests that cells may possess distinct response mechanisms that are selectively activated depending on the mechanical properties of their niche, and the type and dynamics of the mechanical stimulation.

cell biology↗

Biomimetic hydrogel platform reveals active force transduction from retinal pigment epithelium to photoreceptors

In the eye, the retinal pigment epithelium (RPE) maintains the functionality and welfare of retinal photoreceptors and forms a tight, interlocked structure with photoreceptor outer segments (POSs). The RPE-retina interaction is difficult to recapitulate in vitro, limiting the studies addressing the retinal maintenance functions of the RPE. To overcome this challenge, we constructed a retina-mimicking structure using a soft polyacrylamide hydrogel coated with Matrigel. This structure was introduced to RPE cells apical side to model the RPE-retina interface in vitro. As a result, RPE cells attached to the hydrogels during culture, enabling further studies of cell adhesion and force transduction between the RPE-hydrogel with rheology and traction force microscopy. These methods were applied to a critical interactive process between the retina and the RPE: phagocytosis of the aged tips of POSs enabling their renewal. During phagocytosis, RPE cells imposed considerable traction forces to the POS particles. The force generation was actin-dependent, and the forces were significantly reduced by the disruption of RPEs actin cytoskeleton. These results add another layer to the diverse interaction mechanisms between the RPE and the neural retina and pave the way for further studies of the RPE-retina interplay.

biophysics↗

Automated detection of artefacts in neonatal EEG with residual neural networks

Background and ObjectiveTo develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals. MethodsAs part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. ResultsThe Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. ConclusionArtefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms. HighlightsO_LIWe applied a Residual Deep Neural Network as part of an artefact detection algorithm in neonatal electroencephalograms. C_LIO_LIThe algorithm shows high accuracy in identifying artefactual data in general and for specific artefact types. C_LIO_LIEEG channels near the top of the head are less prone to artefact. C_LI

neuroscience↗

Inferior parietal lobule and early visual areas support elicitation of individualized meanings during narrative listening

When listening to a narrative, the verbal expressions translate into meanings and flow of mental imagery, at best vividly immersing the keen listener into the sights, sounds, scents, objects, actions, and events in the story. However, the same narrative can be heard quite differently based on differences in listeners previous experiences and knowledge, as the semantics and mental imagery elicited by words and phrases in the story vary extensively between any given two individuals. Here, we capitalized on such inter-individual differences to disclose brain regions that support transformation of narrative into individualized propositional meanings and associated mental imagery by analyzing brain activity associated with behaviorally-assessed individual meanings elicited by a narrative. Sixteen subjects listed words best describing what had come to their minds during each 3-5 sec segment of an eight-minute narrative that they listened during fMRI of brain hemodynamic activity. Similarities in these word listings between subjects, estimated using latent-semantic analysis combined with WordNet knowledge, predicted similarities in brain hemodynamic activity in supramarginal and angular gyri as well as in cuneus. Our results demonstrate how inter-individual differences in semantic representations can be measured and utilized to identify specific brain regions that support the elicitation of individual propositional meanings and the associated mental imagery when one listens to a narrative.

neuroscience↗