Biology subjects

Hyttinen, J.

Publications and source records attributed to Hyttinen, J..

8 recordsLinked to original sources

bioRxiv2026

Acute scalp EEG signatures of anterior thalamic DBS in epilepsy:reproducible gamma power enhancement within an aperiodic spectral-state shift

Background: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is an established treatment for drug-resistant epilepsy, but acute scalp-EEG signatures of stimulation remain incompletely characterized. Objective: To identify acute scalp-EEG signatures of ANT-DBS and assess their cross-year reproducibility in a proof-of-concept repeated-session cohort. Methods: Scalp EEG from five patients recorded one year apart (10 sessions) was analyzed during nominal cyclic DBS (1 s ON, ~5 s OFF). DBS ON was compared with the within-cycle OFFmean and consecutive post-offset OFF windows using power spectral density (PSD), spatiotemporal-frequency (STF) analysis, and spectral parameterization. Results: DBS ON showed higher 30-70 Hz gamma power than OFFmean in all sessions (median +0.94 dB), with significant PSD clusters overlapping gamma in 10/10 sessions. STF identified gamma increases as the most reproducible spatiotemporal effect. Exploratory frontal 34-44 Hz and central 56-70 Hz components showed cross-year support, and the broader pattern persisted when 48-52 or 45-55 Hz were excluded before clustering. Lower-frequency effects were more heterogeneous and weakened from early to late OFF windows. Aperiodic parameterization showed lower ON exponent and offset in all sessions (Delta chi = -0.20; Delta b = -0.25). After correction, theta suppression was not preserved, whereas a smaller low-gamma residual remained. Conclusions: ANT-DBS was associated with consistent acute scalp-EEG state signatures comprising raw gamma enhancement, lower aperiodic exponent and offset, and a modest residual low-gamma effect. These hypothesis-generating findings show that scalp EEG can capture acute ANT-DBS state changes, while larger prospective studies are needed to establish clinical relevance.

neuroscience↗

bioRxiv2026

Quantitative 3D imaging of intact engineered heart tissues using inverted selective plane illumination microscopy

Abstract Background Advanced in vitro models of the human heart have become promising tools for studying cardiac diseases and drug discovery. Three-dimensional (3D) engineered heart tissues (EHTs) provide more adult cardiac tissue-like structural organization compared to traditional two-dimensional (2D) in vitro models with straightforward contractile readouts. However, the assessment of the architecture of EHTs, especially the quantification of cellular and tissue structural components, has remained poorly studied. Here we demonstrate the use of light-sheet microscopy for structural imaging of intact EHTs and provide quantification of the EHT cellular and subcellular structures. Methods We developed an in-house inverted selective plane illumination microscopy (iSPIM) platform and combined it with iterative indirect immunofluorescence (IT-IF) staining for better signal acquisition. We developed an image-processing and analysis pipeline dedicated to volumetric EHT analysis. Results With this integrated platform, we achieved volumetric imaging of intact EHTs and quantification of 3D tissue architecture, revealing aligned cardiomyocytes within the imaged volume and increasing cardiac nuclei content towards the core of the tissue. The developed methods allowed the detection of heterogeneities between tissue sections, demonstrating the sensitivity required for future disease modeling or maturation studies. Conclusions The developed integrated platform opens new possibilities for volumetric analysis and characterization of 3D tissue structure using iSPIM, providing new insights into EHT architecture and the use of EHTs as disease modeling tools.

bioengineering↗

bioRxiv2023

Sensitivity analysis-guided Bayesian Parameter Estimation for Neural Mass Models: Applications in Epilepsy

It is well established that neural mass models (NMMs) can effectively simulate the mesoscopic and macroscopic dynamics of electroencephalography (EEG), including epileptic EEG. However NMMs are characterized by a high-dimensional parameter space and a lack of knowledge on what NMM parameters can be reliably estimated, thus limiting their application to clinical EEG data. In this article, we analyze the parameter sensitivity of Jansen and Rit NMM (JR NMM) in order to identify the most sensitive NMM parameters for reliable parameter estimation from EEG data. We also propose a joint estimation method for NMM states and parameters based on expectation-maximization combined with unscented Kalman smoother (UKS-EM). Global sensitivity analysis methods including Morris method and Sobol method are used to perform sensitivity analysis. Results from both the Morris and Sobol method show that the average inhibitory synaptic gain, B and the time constant of the average inhibitory post-synaptic potentials (PSPs), b-1 have significant impact on the JR NMM output along with having the least interaction with other model parameters. The UKS-EM method for estimating the parameters B and b is validated using simulations under varying levels of measurement noise. Finally we apply the UKS-EM algorithm to intracranial EEG data from 16 epileptic patients. Our results, both at individual and group-level show that the parameters B and b change significantly between the pre-seizure and seizure period, and between the seizure and post-seizure period, with transition to seizure characterized by decrease in average B and high frequency activity in seizure characterized by an increase in b. These results establish sensitivity analysis guided Bayesian parameter estimation as a powerful tool for reducing the parameter space of high dimensional NMMs enabling reliable and efficient estimation of the most sensitive NMM parameters, with the potential for online and fast tracking of NMM parameters in applications such as seizure tracking and control.

neuroscience↗

bioRxiv2023

Biohybrid restoration of the hippocampal loop re-establishes the non-seizing state in an in vitro model of limbic seizures

ObjectiveThe compromise of the hippocampal loop is a hallmark of mesial temporal lobe epilepsy (MTLE), the most frequent epileptic syndrome in the adult population and the most often refractory to medical therapy. Hippocampal sclerosis is found in >50% of drug-refractory MTLE patients and primarily involves the CA1, consequently disrupting the hippocampal output to the entorhinal cortex (EC). Closed-loop deep brain stimulation (DBS) is the latest frontier to improve drug-refractory MTLE; however, current approaches do not restore the functional connectivity of the hippocampal loop, they are designed by trial-and-error and heavily rely on seizure detection or prediction algorithms. The objective of this study is to evaluate the anti-ictogenic efficacy and robustness of an artificial bridge restoring the dialog between hippocampus and EC. ApproachIn mouse hippocampus-EC slices treated with 4-aminopyridine and in which the Schaffer Collaterals are severed, we established an artificial bridge between hippocampus and EC wherein interictal discharges originating in the CA3 triggered stimulation of the subiculum so to entrain EC networks. Combining quantification of ictal activity with tools from information theory, we addressed the efficacy of the bridge in controlling ictogenesis and in restoring the functional connectivity of the hippocampal loop. Main resultsThe bridge significantly decreased or even prevented ictal activity and proved robust to failure; when operating at 100% of its efficiency (i.e., delivering a pulse upon each interictal event), it recovered the functional connectivity of the hippocampal loop to a degree similar to what measured in the intact circuitry. The efficacy and robustness of the bridge stem in mirroring the adaptive properties of the CA3, which acts as biological neuromodulator. Significance. This work is the first stepping stone toward a paradigm shift in the conceptual design of stimulation devices for epilepsy treatment, from function control to functional restoration of the salient brain circuits.

neuroscience↗

bioRxiv2022

A Meta-analysis of 2D vs. 3D Ovarian Cancer Cellular Models

Three-dimensional (3D) cancer models are revolutionizing research, allowing for the recapitulation of in vivo like response through the use of an in vitro system, more complex and physiologically relevant than traditional mono-layer culture. Cancers such as ovarian (OvCa), are prone to developing resistance and are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D culture. However current models often fall short of predicted response where reproducibility is limited owing to the lack of standardized methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in genetic profile presented by a vast array of 3D cultures. A meta-analysis of the literature (Pubmed.gov) spanning 2012 - 2022, was used to identify studies with comparable monolayer (2D) counterparts in addition to RNA sequencing and microarray data. From the data 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e. agarose, collagen or Matrigel) compared to 2D cell cultures. Top genes differentially expressed 2D vs. 3D include C3, CXCL1, 2 and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. Top Enriched Gene sets for 2D vs. 3D include IFN- and IFN-{gamma} Response, TNF- signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape as well as identifying key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironment. SummaryOvarian cancer is one of the most lethal forms of female cancers. Cell culture is often the go to model to study the molecular processes of cancer. However, this is an oversimplification of the reality. 3D tissue culture has been developed to address the cell culture limitations and to provide a more realistic model of the system studied. Cells grown in 3D represent better the human tumour microenvironment. This meta-analysis is exploring the use of 3D tissue culture as a model of ovarian cancer. Our analysis shows that ovarian cancer cells grown in 3D exhibit enhanced regulation in processes pertinent to tumour development and progression. We identify a panel of genes associated with specific 3D growth conditions that could be used as conditional markers. Finally, we present an overview of the state-of-art of 3D culture with an extensive profile of the genes and pathways enhanced in ovarian cancer models.

bioinformatics↗

bioRxiv2022

Iterative immunostaining combined with expansion microscopy and image processing reveals nanoscopic network organization of nuclear lamina

Investigation of nuclear lamina architecture relies on super-resolved microscopy. However, epitope accessibility, labeling density, and detection precision of individual molecules pose challenges within the molecularly crowded nucleus. We developed iterative indirect immunofluorescence (IT-IF) staining approach combined with expansion microscopy (ExM) and structured illumination microscopy to improve super-resolution microscopy of subnuclear nanostructures like lamins. We prove that ExM is applicable in analyzing highly compacted nuclear multiprotein complexes such as viral capsids and provide technical improvements to ExM method including 3D-printed gel casting equipment. We show that in comparison to conventional immunostaining, IT-IF results in a higher signal-to-background -ratio and a mean fluorescence intensity by improving the labeling density. Moreover, we present a signal processing pipeline for noise estimation, denoising, and deblurring to aid in quantitative image analyses and provide this platform for the microscopy imaging community. Finally, we show the potential of signal-resolved IT-IF in quantitative super-resolution ExM imaging of nuclear lamina and reveal nanoscopic details of the lamin network organization - a prerequisite for studying intranuclear structural co-regulation of cell function and fate. (Words: 175)

molecular biology↗

bioRxiv2021

The effect of substrate stiffness on tensile force transduction in the epithelial monolayers

In recent years, the importance of mechanical signaling and the cellular mechanical microenvironment in affecting cellular behavior has been widely accepted. Cells in epithelial monolayers are mechanically connected to each other and the underlying extracellular matrix (ECM), forming a highly connected mechanical system subjected to various mechanical cues from their environment, such as the ECM stiffness. Changes in the ECM stiffness have been linked to many pathologies, including tumor formation. However, our understanding of how ECM stiffness and its heterogeneities affect the transduction of mechanical forces in epithelial monolayers is lacking. To investigate this, we used a combination of experimental and computational methods. The experiments were conducted using epithelial cells cultured on an elastic substrate and applying a mechanical stimulus by moving a single cell by micromanipulation. To replicate our experiments computationally and quantify the forces transduced in the epithelium, we developed a new model that described the mechanics of both the cells and the substrate. Our model further enabled the simulations with local stiffness heterogeneities. We found the substrate stiffness to distinctly affect the force transduction as well as the cellular movement and deformation following an external force. Also, we found that local changes in the stiffness can alter the cells response to external forces over long distances. Our results suggest that this long-range signaling of the substrate stiffness depends on the cells ability to resist deformation. Furthermore, we found that the cells elasticity in the apico-basal direction provides a level of detachment between the apical cell-cell junctions and the basal focal adhesions. Our simulation results show potential for increased ECM stiffness, e.g. due to a tumor, to modulate mechanical signaling between cells also outside the stiff region. Furthermore, the developed model provides a good platform for future studies on the interactions between epithelial monolayers and elastic substrates. Author summaryCells can communicate using mechanical forces, which is especially important in epithelial tissues where the cells are highly connected. Also, the stiffness of the material under the cells, called the extracellular matrix, is known to affect cell behavior, and an increase in this stiffness is related to many diseases, including cancers. However, it remains unclear how the stiffness affects intercellular mechanical signaling. We studied this effect using epithelial cells cultured on synthetic deformable substrates and developed a computational model to quantify the results better. In our experiments and simulations, we moved one cell to observe how the substrate stiffness impacts the deformation of the neighboring cells and thus the force transduction between the cells. Our model also enabled us to study the effect of local stiffness changes on the force transduction. Our results showed that substrate stiffness has an apparent impact on the force transduction within the epithelial tissues. Furthermore, we found that the cells can communicate information on the local stiffness changes over long distances. Therefore, our results indicate that the cellular mechanical signaling could be affected by changes in the substrate stiffness which may have a role in the progression of diseases such as cancer.

cell biology↗

bioRxiv2019

All-optical electrophysiology refines populations of in silico human iPS-CMs for drug evaluation

High-throughput in vitro drug assays have been impacted by recent advances in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) technology and by contact-free all-optical systems simultaneously measuring action potential (AP) and Ca2+ transient (CaTr). Parallel computational advances have shown that in silico models can predict drug effects with high accuracy. In this work, we combine these in vitro and in silico technologies and demonstrate the utility of high-throughput experimental data to refine in silico hiPS-CM populations, and to predict and explain drug action mechanisms. Optically-obtained hiPS-CM AP and CaTr were used from spontaneous activity and under pacing in control and drug conditions at multiple doses. An updated version of the Paci2018 model was developed to refine the description of hiPS-CM spontaneous electrical activity; a population of in silico hiPS-CMs was constructed and calibrated using the optically-recorded AP and CaTr. We tested five drugs (astemizole, dofetilide, ibutilide, bepridil and diltiazem), and compared simulations against in vitro optical recordings. Our simulations showed that physiologically-accurate population of models can be obtained by integrating AP and CaTr control records. Thus constructed population of models predicted correctly the drug effects and occurrence of adverse episodes, even though the population was optimized only based on control data and in vitro drug testing data were not deployed during its calibration. Furthermore, the in silico investigation yielded mechanistic insights, e.g. through simulations, bepridils more pro-arrhythmic action in adult cardiomyocytes compared to hiPS-CMs could be traced to the different expression of ion currents in the two. Therefore, our work: i) supports the utility of all-optical electrophysiology in providing high-content data to refine experimentally-calibrated populations of in silico hiPS-CMs, ii) offers insights into certain limitations when translating results obtained in hiPS-CMs to humans and shows the strength of combining high-throughput in vitro and population in silico approaches. SignificanceWe demonstrate the integration of human in silico drug trials and optically-recorded simultaneous action potential and calcium transient data from human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) for prediction and mechanistic investigations of drug action. We propose a population of in silico models i) based on a new hiPS-CM model recapitulating the mechanisms underlying hiPS-CM automaticity and ii) calibrated with all-optical measurements. We used our in silico population to predict and evaluate the effects of 5 drugs and the underlying biophysical mechanisms, obtaining results in agreement with our experiments and one independent dataset. This work supports the use of high-content, high-quality all-optical electrophysiology data to develop, calibrate and validate computer models of hiPS-CM for in silico drug trials.

pharmacology and toxicology↗