Computational and Structural Biotechnology Journal 18 (2020) 1287–1300 journal homepage: www.elsevier.com/locate/csbj Review Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates Filippo Piccinini a, Tamas Balassa b, Antonella Carbonaro c, Akos Diosdi b,d, Timea Toth b,d, ⇑ Nikita Moshkov b,e,f, Ervin A. Tasnadi b,g, Peter Horvath b,h,i, a Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy b Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary c Department of Computer Science and Engineering, University of Bologna, Italy d Doctoral School of Biology, University of Szeged, Hungary e Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary f National Research University Higher School of Economics, Moscow, Russia g Doctoral School of Computer Science, University of Szeged, Hungary h Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland i Single-Cell Technologies Ltd., Szeged, Hungary article info abstract Article history: Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D Received 21 February 2020 models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; Received in revised form 22 May 2020 (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxi- Accepted 23 May 2020 cology studies based on multicellular 3D biological models, the so-called ‘‘-oids” (e.g. spheroids, tumor- Available online 3 June 2020 oids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells’ behaviour in the culture. Accordingly, the demand for Keywords: advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we Oncology describe the freely accessible tools that are currently available for biologists and researchers interested Microscopy Cancer Spheroids in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. 3D Segmentation In addition, using publicly available nuclear stained datasets we quantitatively compare the segmenta- Single-cell Analysis tion performance of 9 specific tools. Ó 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). Contents 1. Introduction . ..................................................................................................... 1288 2. Freely available software tools for single cell analysis in 3D multicellular -oids. ............................................ 1288 2.1. Iterative thresholding algorithm of the 3D ImageJ Suite (IT3DImageJSuite) . .................................. 1288 2.2. Lines-of-Sight decomposition (LoS) . ........................................................................ 1290 2.3. Modular Interactive Nuclear Segmentation (MINS) . ..................................................... 1290 2.4. OpenSegSPIM. ........................................................................................ 1290 2.5. Real-time Accurate Cell-shape Extractor (RACE) . ..................................................... 1290 2.6. Software for Automated Morphological Analysis (SAMA). ..................................................... 1291 2.7. 3D Visualization-Assisted Analysis software suite (Vaa3D) . ..................................................... 1291 2.8. 3D-Cell-Annotator . ........................................................................................ 1291 2.9. XPIWIT . ........................................................................................ 1291 3. Further freely available tools. .................................................................................. 1291 3.1. Group I: Tools without object splitting algorithms. ..................................................... 1292 3.1.1. Ilastik. ............................................................................................. 1292 ⇑ Corresponding author at: Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary. E-mail address: [email protected] (P. Horvath). https://doi.org/10.1016/j.csbj.2020.05.022 2001-0370/Ó 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 1288 F. Piccinini et al. / Computational and Structural Biotechnology Journal 18 (2020) 1287–1300 3.1.2. Markov Random Fields 3D (MRF3D) . ................................................................ 1292 3.2. Group II: Unsupported or currently unavailable tools . ........................................................... 1292 3.2.1. CellFrost . ................................................................................ 1292 3.2.2. CellSegmentation3D . ................................................................................ 1292 3.2.3. FARSIGHT . ................................................................................ 1292 3.2.4. Pipeline for Automated oR interactive SegMentation of Images (Parismi) . .......................... 1292 3.3. Group III: 3D single-cell analysis tools incompatible with the test environment . ........................................ 1292 3.3.1. Biologically Constrained optimization based cell Membrane Segmentation (BCOMS). .......................... 1293 3.3.2. CellDissect. ................................................................................ 1293 3.3.3. LimeSeg . ................................................................................ 1293 3.3.4. PArameter-free Generic Iterative Thresholding Algorithm (PAGITA). ............................................. 1293 3.3.5. Repulsive parallel Hill-Climbing (RPHC), RoiEdit3D, and SPF-CellTracker . .......................... 1293 3.3.6. 3D Membrane Morphological Segmentation (3DMMS) . ............................................. 1294 3.4. Group IV: Deep-learning tools for 3D segmentation. ........................................................... 1294 3.4.1. CDeep3M. ................................................................................ 1294 3.4.2. QCA Net. ................................................................................ 1294 3.4.3. StarDist . ................................................................................ 1294 3.4.4. 3D U-Net . ................................................................................ 1294 3.4.5. Zhao et al. 2018 approach ................................................................................ 1294 4. Commercial tools . ........................................................................................ 1295 5. General purpose image analysis software . ..................................................................... 1295 6. Tool comparison. ........................................................................................ 1295 7. Summary and outlook . ........................................................................................ 1298 CRediT authorship contribution statement . ..................................................................... 1298 Acknowledgments . ........................................................................................ 1299 Funding. ........................................................................................................ 1299 Conflicts of interest statement . ........................................................................................ 1299 Appendix A. Supplementary data . ..................................................................... 1299 References . ........................................................................................................ 1299 1. Introduction ticular, we have considered and tested the tools that are currently freely available for the scientific community. we should emphasize The gap between standard in vitro cell cultures and complex that segmenting nuclei in microscopy images is typically the first in vivo models is getting tighter nowadays. Multicellular three- step of any quantitative analysis tasks. Numerous international dimensional (3D) in vitro models, the so-called ‘‘-oids” (e.g. spher- competitions, including the ISBI Cell Tracking Challenge [7,8] and oids, tumoroids, organoids) have become widely known in the sci- the Kaggle 2018 Data Science Bowl [9], have challenged the bioin- entific community. They are currently used in numerous anti- formatics community to further improve the available solutions. cancer drug screenings and toxicology studies, and it has been To compare the different tools in real practical scenarios, we extensively demonstrated that they are more reliable than classical used two publicly available nuclear stained datasets related to a flat (2D) cell cultures [1]. cancer spheroid and a mouse embryo, imaged with a light-sheet A geometry- and, consequently, functional complexity-based fluorescence microscope (LSFM) and a confocal microscope, definition of various typologies of 3D multicellular models is respectively. The tools we tested were randomly assigned to 5 reported in [2]. Briefly: multicellular aggregate is the general