FIMM Monthly Publication Highlight
January 2018: Intelligent image-based single cell isolation
Every month we publish a publication highlight that summarises some of the research taking place at FIMM.
A research team led by FIMM FiDiPro Fellow Peter Horvath has developed a novel technique for automating the target selection and isolation process of single cells. The new method utilizes high-resolution microscopy, laser capture microdissection, image analysis and machine learning.
Tissues and cell populations are known to be highly heterogeneous and the differences between individual cells can be biologically very relevant. Yet, majority of the research done is based on measuring the average properties of large cell populations.
One of the bottlenecks of single cell analytics is that single cell isolation, the process of targeting and collecting individual cells for further studies, is still technically challenging. To overcome this challenge, Peter Horvath’s research team aimed to develop a technique that can increase the accuracy and throughput of microscopy-based single-cell isolation by automating the target selection and isolation processes.
This new technology, called computer-assisted microscopy isolation (CAMI), was described in a recent issue of a high profile science journal, Nature Communications.
CAMI combines image analysis algorithms, machine-learning, and high-throughput microscopy to recognize individual cells in suspensions or tissue. Furthermore, it automatically guides cell extraction through laser capture microdissection or micromanipulation.
– Computer-driven automation increases throughput over manual techniques by orders of magnitude, and microscopy-based isolation boasts several advantages over conventional high-throughput isolation techniques, said Peter Horvath.
In addition to the increased throughput, CAMI has also other clear advantages, such as high precision and versatility. With this method, individual cells can be non-disruptively collected from fixed tissue or cell culture and cells can be selected based on phenotypic morphology or location within the tissue.
The team also conducted three proof-of-principle experiment sets to demonstrate the capabilities of the new approach. All these experiments required targeted single-cell isolation and collecting individual cells without disturbing their microenvironment. The cells isolated with this method were successfully used for digital PCR and next-generation sequencing.
– Our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample, Peter Horvath concluded.
The study was done in collaboration with University of Szeged (Hungary), Biological Research Centre of the Hungarian Academy of Sciences, KTH Royal Institute of Technology and FIMM, University of Helsinki.
Brasko C, Smith K, Molnar C, Farago N, Hegedus L, Balind A, Balassa T, Szkalisity A, Sukosd F, Kocsis K, Balint B, Paavolainen L, Enyedi MZ, Nagy I, Puskas LG, Haracska L, Tamas G, Horvath P. Intelligent image-based in situ single-cell isolation. Nat Commun. 2018 Jan 15;9(1):226. doi: 10.1038/s41467-017-02628-4.