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Biology subjects

Fredriksson, S.

Publications and source records attributed to Fredriksson, S..

4 recordsLinked to original sources

CD4 T cell surface architecture is associated with response to anti-PD-1 therapy in non-small cell lung cancer

Anti-PD-1 therapy has substantially improved outcomes for patients with advanced non-small cell lung cancer (NSCLC), but clinical responses remain highly heterogeneous and are difficult to predict. Current immune profiling approaches primarily characterize immune cell composition and protein abundance, whereas the spatial organization of proteins at the cell surface has remained largely unexplored. Here, we demonstrate for the first time that cell surface protein organization is associated with clinical response to immune checkpoint inhibition therapy. Using the Proximity Network Assay (PNA), we simultaneously quantified cell frequencies, protein abundance, and protein organization in pretreatment peripheral blood mononuclear cells from 62 patients with advanced NSCLC treated with anti-PD-1 agents. While most cell populations were unchanged between responders and non-responders, pronounced response-associated alterations were identified within the CD4 T-cell compartment. Responders exhibited elevated expression of adhesion-associated proteins, and remodeling of a CD5-containing membrane domain. Increased colocalization within this domain was associated with poor clinical outcome and reduced survival. Machine learning analysis further identified CD4 T cell features as the strongest predictors of treatment response and demonstrated that protein organization provided greater predictive power than protein abundance alone. Together, these findings identify cell surface protein organization as a previously unexplored source of biomarkers and highlight its potential to improve immune profiling and biomarker discovery in cancer immunotherapy.

immunology↗

Comparative regulomics provides novel insight into the evolution of wood formation across dicot and conifer trees

Understanding the regulatory program underlying wood formation is key to improving biomass production and carbon sequestration in trees. However, how wood formation evolved and how these programs have been rewired across lineages remains unclear. Here, we present the first high-spatial-resolution evo-devo resource spanning the wood transcriptomes of six tree species - three dicots and three conifers - capturing 250 million years of evolutionary divergence. Using orthology-aware co-expression network analysis, we identified genes with conserved and lineage-specific expression patterns. By integrating chromatin accessibility data and transcription factor motif analysis, we further inferred regulatory networks for xylem differentiation and secondary cell wall formation. We demonstrate how this dataset can be used to answer long standing questions in wood biology related to differences in acetylation of cell wall polymers and master regulators of xylem specification across dicot and conifer tree species. The data offer a foundational resource for the tree biology and evo-devo communities, and are publicly available at PlantGenIE.org.

genomics↗

Single-Cell Protein Interactomes by the Proximity Network Assay

Cellular functions depend on dynamic interactions of proteins and their spatial organisation. While transcriptomic and proteomic methods of molecular parts-lists have enabled single-cell profiling based on abundance, scalable technologies allowing high-resolution measurements of protein organization and interactions at scale are lacking. Here we present the Proximity Network Assay (PNA), a DNA-based method for constructing three-dimensional nanoscale maps of 155 plasma membrane proteins in single cells without the use of optics. PNA employs barcoded antibodies and in situ rolling circle amplification to generate >40,000 spatial nodes per cell, which are linked through proximity-dependent gap-fill ligation and decoded by DNA sequencing forming single cell Proximity Networks. PNA captures abundance, self-clustering, and [~]12,000 pairwise colocalization relationships per single-cell, validating established protein interactions. This new modality provides a framework to uncover novel spatial biomarkers, reveal functional mechanisms, and advance translational studies in immunology, oncology, and cell therapy.

bioengineering↗

Molecular Pixelation: Single cell spatial proteomics by sequencing

The spatial distribution of cell surface proteins govern vital processes of the immune system such as inter-cell communication and mobility. However, tools for studying these at high multiplexing scale, resolution, and throughput needed to drive novel discoveries are lacking. We present Molecular Pixelation, a DNA-sequencing based method for single cell analysis to quantify protein abundance, spatial distribution, and colocalization of targeted proteins using Antibody Oligonucleotide Conjugates (AOCs). Relative locations of AOCs are inferred by sequentially associating these into local neighborhoods using DNA-pixels containing unique pixel identifier (UPI) sequences, forming >1,000 connected spatial zones per single cell in three dimensions. DNA-sequencing reads are computationally arranged into spatial single cell maps for 76 proteins without cell compartmentalization. By studying immune cell dynamics and using spatial statistics on graph representations of the data, previously known and novel patterns of protein spatial polarization and co-localization were found in chemokine-stimulated T-cells.

immunology↗