Biology subjects

Fink, L.

Publications and source records attributed to Fink, L..

4 recordsLinked to original sources

bioRxiv2025

Cytokine mRNA Delivery and Local Immunomodulation in the Placenta using Lipid Nanoparticles

During pregnancy, the maternal immune system adapts to balance tolerance of the semi-allogenic fetus while protecting the fetus from pathogens. Dysregulated immune activity at the maternal-fetal interface contributes to pregnancy complications, such as recurrent pregnancy loss and preeclampsia. Compared to healthy placentas, preeclamptic placentas exhibit increased pro-inflammatory signaling, including a predominance of inflammatory macrophages, leading to impaired tissue remodeling and restricted blood flow. However, the precise mechanisms driving this immune imbalance remain poorly understood, in part due to the lack of tools to probe individual pathways. Here, we use lipid nanoparticles (LNPs) to deliver cytokine-encoded mRNA to placental cells, called trophoblasts, enabling local immunomodulation. LNP-mediated delivery of IL-4 and IL-13 mRNA induced cytokine secretion by trophoblasts, leading to polarization of primary human monocytes toward anti-inflammatory phenotypes. Notably, lowering the mRNA dose increased expression of alternatively-activated macrophage markers, revealing an inverse relationship between dose and polarization status. Intravenous injection of LNPs in pregnant mice achieved placental secretion of IL-4 and IL-13 with minimal changes to pro-inflammatory cytokines in the serum. These findings establish LNPs as a tool for local immunomodulation in the placenta, offering a strategy to study and treat immune dysfunction in pregnancy and in other inflammatory conditions.

bioengineering↗

bioRxiv2019

Restored alveolar epithelial differentiation and reversed human lung fibrosis upon Notch inhibition

Alveolar epithelial cell type II (AEC2) injury underlies idiopathic pulmonary fibrosis (IPF). Here we show increased Notch1 signaling in AEC2s in human IPF and IPF models, causing enhanced proliferation and de-differentiation of AEC2s. As a result, we observed defective surfactant protein (SP)-B/C processing, elevated alveolar surface tension, repetitive alveolar collapse and development of lung fibrosis. Similar changes were encountered upon pharmacological inhibition of SP-B/C processing in vivo by pepstatin A. Inhibition of Notch signaling in cultured human IPF precision cut lung slices improved surfactant processing capacity of AEC2s and reversed fibrosis. Notch1 therefore offers as novel therapeutic target.\n\nOne sentence summaryNotch1 inhibition restores alveolar epithelial differentiation and surface tension and reverses matrix deposition in lung fibrosis

cell biology↗

bioRxiv2019

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing

The libraries generated by high-throughput single cell RNA-sequencing platforms such as the Chromium from 10X Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use this data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3 libraries consisting of over 70,000 cells. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and cell, UMI, and gene detection. However, compared with the NextSeq 500, the MGISEQ-2000 platform performs consistently better, identifying more cells, genes, and UMIs at equalised read depth. We were able to call an additional 1,065,659 SNPs from sequence data generated by the BGI platform, enabling an additional 14% of cells to be assigned to the correct donor from a multiplexed library. However, both the NextSeq 500 and MGISEQ-2000 detected similar frequencies of gRNAs from a pooled CRISPR single cell screen. Our study provides a benchmark for high capacity sequencing platforms applied to high-throughput single cell RNA-seq libraries.

genomics↗

bioRxiv2018

Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

Single-cell time-lapse studies have advanced the quantitative understanding of cell-to-cell variability. However, as the information content of individual experiments is limited, methods to integrate data collected under different conditions are required.\n\nHere we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.

systems biology↗