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Nephrotoxicity Testing with Bioprinted Renal Spheroids
4. Conclusion and outlook https://doi.org/10.1016/j.drudis.2012.10.003

The presented results successfully demonstrated a concept 2. Homan KA, Kolesky DB, Skylar-Scott MA, et al., 2016.
for automated toxicity testing with renal spheroids. 3D Bioprinting of 3D Convoluted Renal Proximal Tubules on
bioprinting technology enabled scalable, reproducible, Perfusable Chips. Sci Rep, 6:34845.
and automated fabrication of renal spheroids. In a head-to- https://doi.org/10.1038/srep34845
head comparison, functional differences between 2D and 3. Secker PE, Luks L, Schlichenmaier N, et al., 2018, RPTEC/
3D cell models were found. Toxic treatment effects varied TERT1 Cells form Highly Differentiated Tubules when
with time and quantity, indicating increased sensitivity to
the specific toxicant. This clearly demonstrated how 3D Cultured in a 3D Matrix. ALTEX, 35:223.
cell models could be of increasing relevance for future https://doi.org/10.14573/altex.1710181
applications, such as toxicity studies. Deep learning 4. Kaminski MM, Tosic J, Kresbach C, et al., 2016. Direct
image classification enabled an automated image-based Reprogramming of Fibroblasts into Renal Tubular
readout. Although relatively high accuracies were Epithelial Cells by Defined Transcription Factors. Nat Cell
achieved in this study, further improvements could be Biol, 18:1269.
implemented in the future. To improve the performance, https://doi.org/10.1038/ncb3437
the present dataset could be augmented with additional
images of the cell morphologies. Therefore, more 5. Buzhor E, Harari-Steinberg O, Omer D, et al., 2011, Kidney
biomarkers and cell viability assays (e.g., MTT, Spheroids Recapitulate Tubular Organoids Leading to
[13]
PrestoBlue, etc.) could be applied to generate ideally Enhanced Tubulogenic Potency of Human Kidney-derived
[21]
multi-channel and -dimensional fluorescence images, Cells. Tissue Eng Part A, 17:2305.
which could then be used to train the algorithm. With this https://doi.org/10.1089/ten.TEA.2010.0595
additional image information, an increased performance
could potentially be achieved, which would contribute 6. Mota C, Camarero-Espinosa S, Baker MB, et al., 2020,
to image-based toxicity readouts with higher precision Bioprinting: From Tissue and Organ Development to In Vitro
in the future. Models. Chem Rev, 120:10547.
https://doi.org/10.1021/acs.chemrev.9b00789
Acknowledgments 7. Gutzweiler L, Kartmann S, Troendle K, et al., 2017,
This work was supported by the Baden-Württemberg Large Scale Production and Controlled Deposition of
Stiftung (Grant No IAF-3). The authors acknowledge to Single HUVEC Spheroids for Bioprinting Applications.
S.S.L. and S.Z,. and the European Research Council to Biofabrication, 9:25027.
S.S.L. (grant agreement No 804474, DiRECT), and the https://doi.org/10.1088/1758-5090/aa7218
Swiss National Science Foundation to S.S.L. (NCCR
Kidney.CH). 8. Tröndle K, Rizzo L, Pichler R, et al., 2021, Scalable
Fabrication of Renal Spheroids and Nephron-like Tubules by
Conflict of interest Bioprinting and Controlled Self-assembly of Epithelial Cells.
The authors have no conflicts to declare. Biofabrication, 13:185.
https://doi.org/10.1088/1758-5090/abe185
Author contributions 9. Ng WL, Chan A, Ong YS, et al., 2020, Deep Learning for
K.T., G.M., S.Z., R.Z., S.L., R.P., and S.K. provided the Fabrication and Maturation of 3D Bioprinted Tissues and
conceptual design. S.L. and S.Z. were responsible for Organs. Virtual Phys Prototyp, 15:340.
formal analysis. S.K., S.Z., R.Z., P.K., and S.L. acquired https://doi.org/10.1080/17452759.2020.1771741
funding. K.T., G.M., L.R., R.P., and F.K. conducted 10. Yu C, Jiang J, 2020, A Perspective on Using Machine
experiments including cell culture, bioprinting, imaging,
and investigation. F.K., K.T., R.P., L.R., P.K., and S.Z. Learning in 3D Bioprinting. Int J Bioprint, 6:253.
provided resources. S.Z.; R.Z. supervised the work. https://doi.org/10.18063/ijb.v6i1.253
K.T., G.M., and S.Z. were responsible for writing and 11. Benning L, Peintner A, Finkenzeller G, et al., 2020,
visualization. Automated Spheroid Generation, Drug Application and
Efficacy Screening Using a Deep Learning Classification:
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