Biology subjects

Sorenson, J.

Publications and source records attributed to Sorenson, J..

3 recordsLinked to original sources

bioRxiv2025

Comparative proteomic analysis of the ECM composition of the human omentum and mesentery, the main sites of ovarian cancer metastasis

Due to its limited symptoms, high-grade serous ovarian cancer (HGSOC) has frequently metastasized extensively throughout the peritoneal cavity prior to its diagnosis, resulting in an overall five-year survival rate of less than 50%. The greater omentum and the small bowel mesentery are two of the most common metastatic sites in advanced HGSOC. However, the mechanisms underlying HGSOC metastatic tropism remain unknown. The extracellular matrix is a complex and dynamic meshwork of proteins that provides biochemical and mechanical signals to surrounding cells and has been shown to drive the dissemination of several cancer types to preferential distant sites. Here, using histological assessment and proteomics, we examined the composition of the extracellular matrix of paired omentum and mesentery samples from disease-free adult females. We found that the fibrillar collagen content of the mesothelial layer of the omentum was significantly higher than that of the mesentery. Using ECM-focused proteomics, we further defined the ECM composition - or matrisome - of these two tissues. We found that over 90% of the proteins detected were shared between the omentum and mesentery. Our analysis also revealed small subsets of tissue-specific ECM proteins. Future work will aim to test the possible functional contributions of these ECM proteins to HGSOC metastatic tropism. To facilitate the reuse of our dataset, we have deposited the raw mass spectrometry data and accompanying metadata files to the ProteomeXchange Consortium with the dataset identifier PXD061586.

cancer biology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2019

Resolving sub-clonal heterogeneity within cell-line growths by single cell sequencing genomic DNA

We performed shallow single-cell sequencing of genomic DNA across 1,475 cells from a well-studied cell-line, COLO829, to resolve overall tumor complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and provides a benchmark for evaluating somatic alterations, though has exhibited conflicting and indeterminate copy number states. We identified at least four major sub-clones by discriminant analysis of principal components (DAPC) of single cell copy number data. Break-point and loss of heterozygosity (LOH) analysis of aggregated data from sub-clones revealed a complex rearrangement of chromosomes 1, 10 and 18 that was maintained in all but two sub-clones. Likewise, two of the sub-clones were distinguished by loss of 1 copy of chromosome 8. Re-analysis of previous spectral karyotyping data and bulk sequencing data recapitulated these sub-clone hallmark features and explains why the original bulk sequencing experiments generated conflicting copy number results. Overall, our results demonstrate how shallow copy number profiling together with clustering analysis of single cell sequencing can uncover significant hidden insights even in well studied cell-lines.

genomics↗