BIAS

BIAS, SCT

BIAS @ CRUK

This Monday, Single-Cell Technologies (SCT) had the incredible honor of presenting our BIAS platform at Cambridge University for the Deep Visual Proteomics seminar organized by the CRUK Cambridge Institute, sharing

BIAS, Publication

Pathology of transplanted heart rejection using artificial intelligence-based image analysis of endomyocardial biopsies

Szferle et al. have developed an AI-based pathology workflow to objectively quantify and predict heart transplant rejection from biopsy images . This study, published in the Hungarian medical journal Orvosi Hetilap, centered on optimizing and applying our Biological Image Analysis Software (BIAS) to automatically identify cells and measure key morphological parameters that indicate rejection . The research, involving team members from our company, Single-Cell Technologies, successfully demonstrated that BIAS can quantify parameters like lymphocyte density and proximity to heart muscle cells, which strongly correlate with the severity of graft rejection, offering a new and powerful quantitative tool for pathologists .

BIAS, SCT

Single-cell Isolation Workshop

We are excited to announce SCT’s first ever multi-day Single-cell Isolation Workshop to be held at the Single Cell Centre, Szeged, Hungary on the November 10-11, 2025.
Registration is free but places are limited, and will remain open until the end of September 2025.

For details and registration, please visit the event webpage.

BIAS, Custom BIAS, Publication

Deep Visual Proteomics defines single-cell identity and heterogeneity

Mund et al. have developed a revolutionary method called Deep Visual Proteomics (DVP), which for the first time, allows for the analysis of thousands of proteins from single cells while keeping their original location in the tissue intact. Our Biological Image Analysis Software (BIAS) was a key component of this research, providing the powerful AI-driven image analysis needed to identify and classify cells for proteomic analysis. This groundbreaking work, published in Nature Biotechnology, opens up new avenues for understanding the molecular details of diseases like cancer.

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