Sartorius ALS builds automated cell imaging and label-free microscopy instruments for life sciences research, increasingly integrating AI-driven image analysis.
Sartorius Automated Lab Solutions (ALS) is the instrumentation and automation arm of the Sartorius Group, based in Jena, Germany. They design and manufacture automated laboratory systems for life sciences research, with particular expertise in cell imaging, cell picking, and quantitative microscopy platforms. Their contribution to EU research consortia is primarily as a technology provider — supplying advanced instruments such as biophotonic and label-free imaging systems that other partners use for biological experiments. In H2020 projects, they have connected their hardware capabilities with machine learning and AI-driven image analysis to push automated cell biology toward clinical and neuroscience applications.
Both LIMA and REVEAL rely on ALS instrumentation for cell-level imaging — LIMA for circulating tumour cell capture and REVEAL for neuronal cell behavioural observation and manipulation.
REVEAL explicitly involves 3D refractive index microscopy and quantitative phase imaging, techniques that require no fluorescent labels and are an ALS hardware speciality.
LIMA (2018–2022) positioned ALS technology in the cancer diagnostics pipeline, supporting isolation and analysis of circulating tumour DNA and circulating tumour cells.
REVEAL (2021–2025) introduces neural networks and machine learning as part of the cell imaging workflow, indicating ALS is extending beyond pure hardware into AI-assisted analysis.
REVEAL's keyword set includes biophotonics alongside cell live imaging, confirming ALS contributes photonics-based measurement hardware to multidisciplinary consortia.
In their first H2020 project (LIMA, starting 2018), ALS brought their cell isolation and imaging hardware into a clinical cancer context — the keywords are dominated by disease terms (liquid biopsy, ct-DNA, breast cancer, rectal cancer) and suggest ALS was positioned close to the diagnostic application layer. By 2021, with REVEAL, the language shifts entirely toward the instrument itself: biophotonics, 3D refractive index microscopy, quantitative phase imaging, cell picking — the vocabulary of a technology developer rather than a clinical partner. The addition of machine learning and neural networks in the second project signals that ALS is actively building software intelligence into their platforms, moving from pure hardware provision toward integrated instrument-plus-algorithm systems.
ALS is moving toward intelligent, label-free imaging platforms that combine advanced photonics hardware with machine learning — making them an increasingly relevant partner for any consortium needing automated, AI-assisted microscopy in neuroscience, cell biology, or drug development.
ALS participates exclusively as a consortium partner — they have never led an H2020 project — which fits their role as a technology provider rather than a research orchestrator. With 16 unique partners across just 2 projects, they join medium-to-large consortia where their instruments are a shared resource for multiple research groups. This pattern suggests they are a sought-after specialist contributor: partners bring them in for their hardware, not to manage workstreams.
ALS has built connections with 16 unique partners across 6 countries through just 2 projects, suggesting they join broad international consortia rather than working in tight bilateral arrangements. No geographic concentration is identifiable from 2 projects, but their Jena base and Sartorius Group affiliation give them strong ties into the German and wider European life sciences instrumentation ecosystem.
ALS sits at a rare intersection: they are the automated lab instruments division of Sartorius, one of the world's leading bioprocessing and laboratory equipment companies, which means they bring industrial-grade, commercially available platforms into research consortia — not prototype devices. This is meaningful for projects that need reliable, reproducible instrumentation rather than one-off lab builds. Their move into label-free imaging (no fluorescent dyes, no sample modification) combined with AI analysis positions them uniquely for consortia that need non-invasive, high-throughput cell characterisation.
