Biology subjects

Amend, S.

Publications and source records attributed to Amend, S..

2 recordsLinked to original sources

bioRxiv2026

A microfluidic platform for multi-marker profiling of extracellular vesicles from single-cell-derived clones

Extracellular vesicles (EVs) carry molecular cargo that can reflect the real-time state of parental cells, yet most in vitro EV analyses rely on bulk approaches and therefore average over pronounced heterogeneity in both cell and EV populations. Here, we present a semi-open microfluidic platform that enables multi-marker profiling of EVs released from single-cell-derived clones, allowing EV signatures to be linked to clonal progeny originating from a single parental cell. The platform integrates aligned cell and EV arrays containing 17,305 wells, assembled with a 3D-printed housing to capture released EVs in one-to-one matched wells. Captured EVs are immunolabeled for canonical tetraspanin markers (CD9, CD63, CD81) and EpCAM, imaged by high-resolution fluorescence microscopy, and quantified using an automated image-analysis pipeline. Applying the platform to single-cell-derived PC3 clones revealed substantial heterogeneity in EV marker co-expression, with hierarchical clustering identifying four distinct tetraspanin co-expression profiles. The fraction of EpCAM-positive EVs increased with PC3 cell proliferation, as assessed by endpoint cell number, whereas free (non-EV-associated) EpCAM showed no correlation. This platform enables near single-EV-level, multi-marker profiling from single-cell lineages and provides a practical approach to simultaneously dissect both cellular and EV heterogeneity.

bioengineering↗

bioRxiv2024

Single-Cell Proteomic and Transcriptomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput Data Independent Acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This optimized DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1,300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by integrating SCP and single-cell RNA-seq (scRNA-seq) data, we identified six upregulated and ten downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

cancer biology↗