Probabilistic cell seeding and non-autofluorescent 3D-printed structures as scalable approach for multi-level co-culture modeling

• To model complex biological tissue in vitro, a specific layout for the position and numbers of each cell type isnecessary. Establishing such a layout requires manual cell placement in three dimensions (3D) with micrometricprecision, which is complicated and time-consuming. Moreover, 3D printed materials used in compartmentalizedmicrofluidic models are opaque or autofluorescent, hindering parallel optical readout and forcing serial charac-terization methods, such as patch-clamp probing. To address these limitations, we introduce a multi-level co-culture model realized using a parallel cell seeding strategy of human neurons and astrocytes on 3D structuresprinted with a commercially available non-autofluorescent resin at micrometer resolution. Using a two-stepstrategy based on probabilistic cell seeding, we demonstrate a human neuronal monoculture that forms net-works on the 3D printed structure and can establish cell-projection contacts with an astrocytic-neuronal co-cultureseeded on the glass substrate. The transparent and non-autofluorescent printed platform allows fluorescence-based immunocytochemistry and calcium imaging. This approach provides facile multi-level compartmentaliza-tion of different cell types and routes for pre-designed cell projection contacts, instrumental in studying complextissue, such as the human brain.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Nanoteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Nano-technology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Industriell bioteknik -- Biomaterial (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Industrial Biotechnology -- Bio Materials (hsv//eng)

NATURVETENSKAP  -- Biologi -- Cellbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Cell Biology (hsv//eng)

Keyword

Two-photon polymerization Neurons Astrocytes Calcium imaging Co-culture models IP-Visio

Publication and Content Type

ref (subject category)

art (subject category)