Biology subjects

Geiger, P.

Publications and source records attributed to Geiger, P..

6 recordsLinked to original sources

bioRxiv2026

From Routine Pathology to Precision Oncology: Automated FFPE Tissue Processing for Large-Scale Molecular Studies

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a3ea5dorg.highwire.dtl.DTLVardef@6fb8edorg.highwire.dtl.DTLVardef@d60a4dorg.highwire.dtl.DTLVardef@3e47b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

APOE4 drives widespread changes to the hepatic proteome and alters metabolic function

Apolipoprotein E (APOE) is essential for lipid homeostasis and has been extensively studied in the central nervous system, particularly in the context of Alzheimers disease (AD). Individuals carrying an APOE4 allele have an increased risk of AD and exhibit deficits in energy metabolism, including glucose utilization and mitochondrial dysfunction. While the role of APOE in the liver is well characterized, the impact of APOE genetic variation on hepatic health and metabolism remains poorly understood. We sought to investigate this using young female and male APOE3 and APOE4 targeted replacement mice. We also used APOE isogenic induced pluripotent stem cell (iPSC)-derived hepatocyte-like cells (iHLCs) to examine specific effects in a human-relevant cell model. Proteomic and functional assays show that APOE4 causes extensive changes to liver mitochondrial function in a sex-specific manner in mice and alters glucose and lipid metabolism. APOE4 also impairs mitochondrial function in iHLCs and shifts metabolism towards glycolysis while modifying expression of extracellular matrix proteins. Additionally, APOE4 iHLCs display a greater reliance on fatty acids as an energy source and show increased lipid accumulation. Taken together, our findings show that APOE genetic variation causes mitochondrial dysfunction and rewires hepatic metabolism.

physiology↗

bioRxiv2025

Proteomic and metabolic profiling reveals APOE4-dependent shifts in whole brain, neuronal, and astrocytic mitochondrial function and glycolysis

Apolipoprotein E (APOE) genetic variation is the strongest genetic risk factor for late onset Alzheimers disease (LOAD). Studies on APOE genotype dependent changes have largely focused on amyloid beta (A{beta}) aggregation, disease pathology, and lipid metabolism. Recently, there has been increased interest in the relationship between metabolic function and APOE genetic variation. In this study, we examined how APOE genotype can alter metabolism in the brains of young male and female APOE3 and APOE4 targeted replacement (TR) mice. In combination with this, we also examined cell type-specific differences using induced pluripotent stem cell (iPSC) derived astrocytes and neurons. We found sex and genotype dependent changes to metabolism in the brains of young APOE TR mice. Specifically, APOE4 mice show signs of metabolic stress and compensatory mechanisms in the brain. Using proteomics and stable isotope tracing metabolomics, we found that APOE4 iAstrocytes and iNeurons exhibit signs of inflammation, mitochondrial dysfunction, altered TCA cycle and malate-aspartate shuttle activity, and a metabolic shift toward glycolysis. Taken together, this data indicates APOE4 causes early changes to metabolism within the central nervous system. While this study establishes a relationship between APOE genotype and alterations in bioenergetics, additional studies are needed to investigate underlying mechanisms.

neuroscience↗

bioRxiv2022

Efficient learning in children with rapid GABA boosting after training

It is unclear why and how children learn more efficiently than adults, although inhibitory systems, which play an important role in stabilizing learning, are immature in children. Here, we found that despite a lower baseline concentration of {gamma}-aminobutyric acid (GABA) in early visual cortical areas in children (8 to 11 years old) than adults (18 to 35 years old), children exhibited a rapid boost of GABA immediately after visual training, whereas the concentration of GABA in adults remained unchanged after training. Moreover, behavioral experiments showed that children stabilized visual learning much faster than adults, showing rapid development of resilience to retrograde interference. These results together suggest that inhibitory systems in childrens brains are more dynamic and adapt more quickly to stabilize learning than in adults. One Sentence SummaryChildren learn more efficiently than adults due to faster stabilization of learning with rapid GABA boosting after training.

neuroscience↗

bioRxiv2019

ACValidator: a novel assembly-based approach for in silico validation of circular RNAs

Circular RNAs (circRNAs) are evolutionarily conserved RNA species that are formed when exons back-splice to each other. Current computational algorithms to detect these back-splicing junctions produce divergent results, and hence there is a need for a method to distinguish true positive circRNAs. To this end, we developed ACValidator (Assembly based CircRNA Validator) for in silico validation of circRNAs. ACValidator extracts reads from a user-defined window on either side of the circRNA junction and assembles them to generate contigs. These contigs are aligned against the circRNA sequence to find contigs spanning the backspliced junction. When evaluated on simulated datasets, ACValidator achieved over 80% sensitivity and specificity on datasets with an average of 10 circRNA-supporting reads and with read lengths of at least 100 bp. In experimental datasets, ACValidator produced higher validation percentages for samples treated with ribonuclease R compared to non-treated samples. Our workflow is applicable to non-polyA-selected RNAseq datasets and can also be used as a candidate selection strategy for experimental validations. All workflow scripts are freely accessible on our github page https://github.com/tgen/ACValidator along with detailed instructions to set up and run ACValidator. Author summaryCircular RNAs (circRNAs) are a recent addition to the class of non-coding RNAs and are produced when exons back-splice and form closed circular loops. Although several computational algorithms have been developed to detect circRNAs from RNA sequencing (RNAseq) data, they produce divergent results. We hence developed the software Assembly based Circular RNA Validator (ACValidator) as an orthogonal strategy to separately validate predicted circRNAs in silico. ACValidator takes as input a sequence alignment mapping (SAM) file and the circRNA coordinate(s) to be validated. Reads surrounding the circRNA junction are extracted from the SAM file and assembled to generate contigs. These contigs are then aligned against the circRNA sequence to identify contigs that span the back-spliced junction. We evaluated our workflow on simulated as well as experimental datasets to demonstrate the utility of our approach. ACValidator is implemented in python and is highly computationally efficient, with a run time of less than 2 minutes for an 8 GB SAM file. This workflow is applicable to non-polyA-selected RNAseq datasets and can also be used as a candidate selection strategy for experimental validations.

bioinformatics↗

bioRxiv2018

Circular RNA expression and regulatory network prediction in posterior cingulate astrocytes in elderly subjects

BackgroundCircular RNAs (circRNAs) are a novel class of endogenous, non-coding RNAs that form covalently closed continuous loops and are both highly conserved and abundant in the mammalian brain. A role for circRNAs in sponging microRNAs (miRNAs) has been proposed, but the circRNA-miRNA-mRNA interaction networks in human brain cells have not been defined. Therefore, we identified circRNAs in RNA sequencing data previously generated from astrocytes microdissected from the posterior cingulate (PC) of Alzheimers disease (AD) patients (N=10) and healthy elderly controls (N=10) using four circRNA prediction algorithms - CIRI, CIRCexplorer, find_circ and KNIFE.\n\nResultsOverall, utilizing these four tools, we identified a union of 4,438 unique circRNAs across all samples, of which 70.3% were derived from exonic regions. Notably, the widely reported CDR1as circRNA was detected in all samples across both groups by find_circ. Given the putative miRNA regulatory function of circRNAs, we identified potential miRNA targets of circRNAs, and further, delineated circRNA-miRNA-mRNA networks using in silico methods. Pathway analysis of the genes regulated by these miRNAs identified significantly enriched immune response pathways, which is consistent with the known function of astrocytes as immune sensors in the brain.\n\nConclusionsIn this study, we performed circRNA detection on cell-specific transcriptomic data and identified potential circRNA-miRNA-mRNA regulatory networks in PC astrocytes. Given the known function of astrocytes in cerebral innate immunity and our identification of significantly enriched immune response pathways, the circRNAs we identified may be associated with such key functions. While we did not detect recurrent differentially expressed circRNAs in the context of healthy controls or Alzheimers, we report for the first time circRNAs and their potential regulatory impact in a cell-specific and region-specific manner in aged subjects. These predicted regulatory network and pathway analyses may help provide new insights into transcriptional regulation in the brain.

neuroscience↗