Biology subjects

Schoenmakers, S.

Publications and source records attributed to Schoenmakers, S..

4 recordsLinked to original sources

bioRxiv2025

Characterization of cytotrophoblast cell population dynamics throughout pregnancy

To understand cytotrophoblast (CTB) dynamics during pregnancy, we constructed a single-cell RNA sequencing dataset profiling over 35,000 CTBs isolated from human placental tissues at various gestational stages. Our analysis revealed a robust set of transcripts defining distinct CTB subsets and their spatial relationships within the placenta. We identified a transit-amplifying primitive trophoblast population that may contribute to the CTB pool and found that CTBs located at the smooth chorion form a leak-tight epithelial layer that adapts to low oxygen environments without undergoing syncytialization. Additionally, our study suggests that CTB fate in the villous chorion may be influenced by interactions with the basal lamina, as indicated by a population of BCAM+ CTBs. This subset, isolated from term placental tissue, displayed progenitor capabilities in organoid cultures, supporting the hypothesis that BCAM+ CTBs maintain a progenitor role late in pregnancy.

cell biology↗

bioRxiv2024

Genome-wide methylation profiling of cell-free DNA in maternal plasma using Methylated DNA Sequencing (MeD-seq)

BackgroundPlacental-originated cell-free DNA (cfDNA) provides unique opportunities to study (epi)genetic placental programming remotely, but studies investigating the cfDNA methylome are scarce and usually technologically challenging. Methylated DNA sequencing (MeD-seq) is well-compatible with low cfDNA concentrations and has a high genome-wide coverage. We therefore aim to investigate the feasibility of genome-wide methylation profiling of first trimester maternal cfDNA using MeD-seq, by identifying placental-specific methylation marks in cfDNA. MethodsWe collected cfDNA from non-pregnant controls (female n=6, male n=12) and pregnant women (n=10), first trimester placentas (n=10), and paired preconceptional and first trimester buffy coats (total n=20). Differentially methylated regions (DMRs) were identified between pregnant and non-pregnant women. We investigated placental-specific markers in maternal cfDNA, including RASSF1 promoter and Y-chromosomal methylation, and studied overlap with placental and buffy coat DNA methylation. ResultsWe identified 436 DMRs between cfDNA from pregnant and non-pregnant women which were validated using male cfDNA. RASSF1 promoter methylation was higher in maternal cfDNA (fold change 2.87, unpaired t-test p<0.0001). Differential methylation of Y-chromosomal sequences could determine fetal sex. DMRs in maternal cfDNA showed large overlap with DNA methylation of these regions in placentas and buffy coats, indicating a placental and immune-cell contribution to the pregnancy-specific cfDNA methylation signature. Sixteen DMRs in maternal cfDNA were specifically found only in placentas. These novel potential placental-specific DMRs were more prominent than RASSF1. ConclusionsMeD-seq can detect (novel) genome-wide placental DNA methylation marks and determine fetal sex in maternal cfDNA. This study supports future research into maternal cfDNA methylation using MeD-seq. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/610227v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@b8fd55org.highwire.dtl.DTLVardef@ffe942org.highwire.dtl.DTLVardef@12afbf4org.highwire.dtl.DTLVardef@1036a6a_HPS_FORMAT_FIGEXP M_FIG C_FIG Studies investigating the maternal cell-free DNA (cfDNA) methylome are scarce and generally technologically challenging. We identified 436 autosomal differentially methylated regions (DMRs) between cfDNA from pregnant and non-pregnant women, using the innovative methylated DNA sequencing (MeD-seq) technique. Y-chromosomal methylation could determine fetal sex, we show hypermethylation of the placental-marker RASSF1, and identify 16 novel placental-specific markers in maternal cfDNA including DMRs related to TMEM240, DHRS3, and PCMTD2. This pilot study supports future research into the maternal cfDNA methylome using MeD-seq.

genomics↗

bioRxiv2019

Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. The uncertainty in these dMRI measures is often ignored, while previous work in functional MRI has shown that incorporating uncertainty estimates can lead to group statistics with a higher statistical power. We propose the Fisher Information Matrix (FIM) as a generally applicable method for quantifying the parameter uncertainties in non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo sampling, the FIM produces similar uncertainty estimates at lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, like data complexity and signal-to-noise ratio. In individual subjects, the parameter standard deviations can help in detecting white matter artifacts as patches of relatively large standard deviations. In group statistics, we recommend using the parameter standard deviations by means of variance weighted averaging. Doing so can reduce the overall variance in group statistics and reduce the effect of data artifacts without discarding data from the analysis. Both these effects can lead to a higher statistical power in group studies.

neuroscience↗

bioRxiv2017

CNN-based Encoding and Decoding of Visual Object Recognition in Space and Time

Deep convolutional neural networks (CNNs) have been put forward as neurobiologically plausible models of the visual hierarchy. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feedforward sweep across the visual hierarchy between 75-200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.

neuroscience↗