Kerkela, E. | bioRxiv Science

bioRxiv2026

Feeder cell - the key component in producing scalable and fit NK cells for therapeutic use

Natural killer (NK) cells are increasingly recognized as a versatile therapeutic platform, yet their translation is hindered by limited ex vivo proliferation. Feeder cells serve as robust stimulatory component supplying activating signals required to initiate large-scale NK cell expansion. Here, using bench-scale cultures, we evaluated how distinct engineered K562-based feeder cells influence NK cell proliferation, phenotype maintenance, potential for activation, and post-cryopreservation function. Across conditions, feeder-based systems consistently enabled superior, up to 500-fold higher NK cell yield compared to feeder-free system. Variants incorporating membrane-bound costimulatory and cytokine cues yielded the most favorable balance between expansion and functional preservation. Simple adjustments to cryopreservation, including high-density-freezing and centrifuge-free-thawing, further supported NK cell recovery. Together, these findings highlight feeder cells as essential upstream reagents for effective NK cell bioproduction and provide foundational biological insights to guide the rational design and validation of future scalable NK cell manufacturing platforms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/718880v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@1e8e8baorg.highwire.dtl.DTLVardef@720d4org.highwire.dtl.DTLVardef@1fc7ce2org.highwire.dtl.DTLVardef@16b0fca_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

bioinformatics↗