Biology subjects

Grinnemo, K.-H.

Publications and source records attributed to Grinnemo, K.-H..

3 recordsLinked to original sources

bioRxiv2025

Single-cell proteomics of pancreatic islet cells reveals type 1 diabetes and donor-specific features

Type 1 diabetes mellitus (T1DM) is the most common severe chronic disease in children and adolescents and requires life-long exogenous insulin treatment. Investigating key differences between the pancreas of healthy and T1DM patients is key to help uncover new treatments. Here we performed a single-cell proteomic (SCP) analysis of purified pancreatic islets from several healthy and T1DM donors at a depth of > 4000 protein quantified. We employed tools designed for single-cell mRNA sequencing (scRNAseq) for cell type annotation highlighting discrepancies between mRNA and protein markers. Subsequently, we defined a subset of markers useful for future proteomics analyses of pancreatic islets. This marker list was employed to refine the annotations of our combined dataset, which we exploited with a machine learning approach to successfully transfer annotations into a new islet dataset. Lastly, we showcase the applicability and value of SCP in a clinical context by providing novel insights into cell type-specific differences between healthy and T1DM islets as well as between healthy islets of different donors. Our study is the first SCP analysis of pancreatic islets from multiple donors and T1DM islets, performed at this level of analytical depth. This provides methodological and biological insight as well as a reference dataset for future SCP studies and for any researcher interested in T1DM and in pancreatic islet physiology.

biochemistry↗

bioRxiv2025

Advancing clinical outcome predictions via incorporating pharmacokinetic simulations into in vitro testing - a colorectal cancer example

The development of in vitro assays that can predict clinical outcomes is highly desirable for drug development and personalized medicine. However, conventional in vitro methods often fail to replicate physiological drug pharmacokinetics, posing a challenge to their clinical translation. To address this issue, we adjusted incubation times and concentrations of standard-of-care drugs in the in vitro chemosensitivity assay to reflect those encountered by colorectal cancer patients. Then, for first time, we mimicked the relevant drug exposure of mFOLFOX-6, CapOx and FOLFIRI protocols to predict clinical outcomes. Our pharmacokinetic-based testing on primary colorectal cancer cells accurately predicted responders and non-responders among a cohort of patients (N=6). Classical testing methods such as IC50 and GI50 did not reveal any clinically meaningful results. Furthermore, we demonstrated that even subtle changes in drug incubation times could lead to significant variations in the classification of cells as sensitive and resistant, which is not related to mechanisms of action according to categorical clustering. Finally, our pharmacokinetic-based test results were consistent with the historical clinical data on similarities of mFOLFOX-6 and CapOx schemes. Our results contribute to the growing body of evidence that pharmacokinetic-based in vitro testing could bridge the gap between laboratory research and clinical practice. Integration of pharmacokinetic dynamics into in vitro tests could have a significant potential in enhancing drug development and refining personalized treatment strategies.

pharmacology and toxicology↗

bioRxiv2024

Global analysis of protein turnover dynamics in single cells

Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from [~]4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional measurements beyond protein abundance. This method distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner.

systems biology↗