Biology subjects

Kamp, T.

Publications and source records attributed to Kamp, T..

3 recordsLinked to original sources

bioRxiv2026

Multi-omic analysis reveals maturation programs in human pluripotent stem cell-derived cardiomyocytes during long-term culture

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) hold tremendous promise for disease modeling, drug discovery, and cardiac regenerative therapies. However, the immature phenotype of hPSC-CMs remains a major barrier limiting their translational utility. Here, we performed integrated multi-omic profiling to identify molecular pathways and regulatory programs associated with hPSC-CM maturation during long-term culture. hPSC-CMs were cultured for 113 days and analyzed using metabolomics, proteomics, and transcriptomics across progressive stages of maturation. Long-term culture induced widespread multi-omic remodeling, including significant changes in 142/934 metabolites, 550/3,556 proteins, and 2,892/23,309 transcripts from Day 30 to Day 113. Metabolomic analyses revealed early increases in phospholipid biosynthesis and mitochondrial beta oxidation of fatty acids from Day 30 to Day 60, suggesting metabolic priming precedes later maturation events. In contrast, proteomic remodeling was more prominent during later stages of maturation and was characterized by enhanced calcium handling and cell cycle exit. Transcriptomic analyses demonstrated progressive increases in ion channel expression, t-tubule organization, fatty acid metabolism, creatine shuttle pathways, and cell cycle arrest programs. Transcriptomic and integrative multi-omic pathway analyses identified coordinated suppression of TGF{beta}, MAPK, Wnt, and Hedgehog signaling together with activation of integrin-related, respiratory electron transport, muscle contraction, and Slit-Robo signaling pathways during maturation. Moreover, multi-omic transcription factor activity analysis prioritized a GATA4-centered network of putative cardiomyocyte maturation regulators including SOX7, SOX18, TBX2, and ZFPM2 (FOG2). Together, these findings elucidate the degree and pace of hPSC-CM maturation during long-term culture and establish an integrated multi-omic framework for identifying strategies to accelerate hPSC-CM maturation.

Cell Biology↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗