Jump to content

Flow Cytometry Data Analysis Automation with Genedata Screener

Flow cytometry and FACS are everyday tools used across the large molecule discovery and development workflow. Beginning with hit identification to functional characterization of your lead molecules, flow cytometry is used for key assays such as:

  • Hybridoma / B-cell screening / Binding assays
  • Cross-species reactivity
  • Cytokine assays
  • Antibody-dependent cell cytotoxicity (ADCC)
  • Antibody-dependent cellular phagocytosis (ADCP) 
  • Complement-dependent cytotoxicity (CDC)
  • And more…

Standardization and automation of flow cytometry data analysis saves time and improves productivity. Genedata Screener integrates with industry-standard flow cytometers from Sartorius (including iQue®), BioRad, and BD Biosciences. The software automates data analysis and streamlines reporting, allowing you to move from raw data to results and scale up in one single platform. 

By using Genedata Screener for flow cytometry experiments, you can:

  • Instantly access results at all resolution levels, from single cells to well and plates, for easy comparison and interrogation.
  • Perform population gating with interactive histograms and scatter plots; conduct final result calculations such as automated dose response curve fitting on selected cell subpopulations in one platform. 
  • Instantly process entire campaigns by applying your saved gating and processing settings, and re-process on the fly when settings need adjustment.
  • Report results to a data warehouse in one click.

Want to improve your productivity through automated data analytics? Get more information below:

High Throughput Flow Cytometry with Genedata Screener

Webinar: Automate Flow Cytometry Data Analysis and Report Results with One Click

Flow cytometry is a versatile method for functional antibody screening and characterization in drug discovery. To efficiently contribute to discovery projects, flow assays need to be speedily analyzed and their results integrated with other information on the molecules to take appropriate progression decisions. Genedata Screener® streamlines analysis of these flow assays and combines their results directly with information from large-molecule registration and workflow systems such as Genedata Biologics®.
In this webinar & software demonstration we show you how to:

  • Rapidly template flow cytometry analysis workflows 
  • Automatically run the process, from data capture to end results  
  • Review at-a-glance antibody affinity and cross-reactivity results 
  • Simplify quality assurance with automated rules and filters 
  • Report results and population plots to Genedata Biologics in a single click
Watch Webinar On Demand
High-Throughput Flow Cytometry with Genedata Screener

Poster: Establishing High-Throughput Flow Cytometry in a Screening Informatics Infrastructure

The high acquisition speed of modern flow cytometry instrumentation has made the technology a new asset in the high throughput drug discovery portfolio. The multiplexed readouts support a broad range of critical research applications requiring quantification of cell surface and/or intracellular markers, allowing high-throughput flow cytometry to become a routinely applied technology along the complete discovery process, from phenotypic library screens to lead optimization. However, a data analysis solution suited for the unique requirements of high-throughput screening has so far been missing.

Here we discuss how Genedata Screener® can accelerate the efficiency of flow cytometry data analysis in both small molecule and antibody screening experiments. Genedata Screener combines flow-specific analyses and interactive views with powerful automated data processing and quality control. Fully integrated with the informatics landscape, it enables e.g. automated reporting to corporate warehouses and enterprise access to results and underlying data. The solution fits high throughput flow cytometry seamlessly into established screening workflows and infrastructure, directly funneling results to support compound progression decisions.

Request Poster Now