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Systems Survey of Endocytosis by Multiparametric Image Analysis Vol 464 | 11 March 2010 | doi:10.1038/nature08779 ARTICLES Systems survey of endocytosis by multiparametric image analysis Claudio Collinet1, Martin Sto¨ter2, Charles R. Bradshaw1, Nikolay Samusik1, Jochen C. Rink{, Denise Kenski{, Bianca Habermann1, Frank Buchholz1, Robert Henschel3, Matthias S. Mueller3, Wolfgang E. Nagel3, Eugenio Fava2, Yannis Kalaidzidis1,4 & Marino Zerial1 Endocytosis is a complex process fulfilling many cellular and developmental functions. Understanding how it is regulated and integrated with other cellular processes requires a comprehensive analysis of its molecular constituents and general design principles. Here, we developed a new strategy to phenotypically profile the human genome with respect to transferrin (TF) and epidermal growth factor (EGF) endocytosis by combining RNA interference, automated high-resolution confocal microscopy, quantitative multiparametric image analysis and high-performance computing. We identified several novel components of endocytic trafficking, including genes implicated in human diseases. We found that signalling pathways such as Wnt, integrin/cell adhesion, transforming growth factor (TGF)-b and Notch regulate the endocytic system, and identified new genes involved in cargo sorting to a subset of signalling endosomes. A systems analysis by Bayesian networks further showed that the number, size, concentration of cargo and intracellular position of endosomes are not determined randomly but are subject to specific regulation, thus uncovering novel properties of the endocytic system. Endocytosis is a fundamental process supporting many functions platform, on the basis of the custom-designed image analysis software such as nutrient uptake, intracellular signalling1, morphogenesis MotionTracking12. First, images were segmented to determine mor- during development2 and defence against pathogens3. Dysfunctions phological and positional descriptors for each endosome (Fig. 1b). of the endocytic system lead to severe metabolic4, infectious5 and Second, a total set of 62 parameters were extracted, 4 serving as quality neurodegenerative diseases6. Receptors are internalized by both control to reject sub-optimal images and 58 describing defined bio- Clathrin-dependent and -independent mechanisms into early endo- logical properties of the endocytic system, such as total internal cargo, somes and, from here, follow different intracellular routes. For number, size (mean apparent area) and position of endosomes example, transferrin (TF) is recycled back to the plasma membrane, (Fig. 1c, Supplementary Table 1 and Supplementary Information whereas epidermal growth factor (EGF) is routed to late endosomes/ for details). The latter were processed to suppress plate-to-plate lysosomes for degradation. Despite a great deal of molecular random variations, normalized (see Supplementary Information) insights7–9 our understanding of the endocytic machinery remains and the 46 most robust parameters (see Supplementary Information fragmentary. Furthermore, the design principles of the endocytic and Supplementary Table 1) were combined into multiparametric pathway, its integration in the overall cellular system and high-order profiles (Fig. 1d) describing quantitatively the endocytic phenotypes. control are still largely unknown. Addressing these problems requires In preparation for the genomic screen, we validated the quality and methods capable of seizing the complexity at the system level (that is, reproducibility of assay and QMPIA. First, we verified that the deple- measuring many key parameters). tion of established components of the endocytic machinery, such as Here, we designed a new analytical approach aimed at phenotypi- Clathrin (CLTC), Dynamin-2 (DNM2), EGF receptor (EGFR), cally profiling genes with respect to endocytosis by genome-wide Transferrin receptor (TFRC), early endosome antigen 1 (EEA1) RNAi screening through the quantitative assessment of many system and others by RNAi (Supplementary Figs 1 and 2), yielded the parameters. expected alterations in phenotypic profiles. For example, silencing of TFRC reduced the values of number of endosomes, total internal Multiparametric profiling of the endocytic system cargo and mean cargo load per endosome for TF but not EGF (Fig. 1d We visualized endocytosis of fluorescent TF and EGF in HeLa cells and Supplementary Fig. 1e). Conversely, EGFR downregulation per- before we could apply our platform to more disease-relevant systems. turbed the parameters of EGF but not of TF (Supplementary Fig. 1e). After small interfering RNA (siRNA) transfection, cells internalized Second, we validated the use of a single confocal section as representa- the two cargo markers simultaneously for 10 min, were fixed, stained tive of the three-dimensional endosomal population of the entire cell with 49,6-diamidino-2-phenylindole (DAPI)/SYTO42 blue for nuclei (Supplementary Fig. 3). Third, we validated the reproducibility of the and cytoplasm detection, and imaged (12 images per well) by triple- observed phenotypic profiles using the same and different siRNAs colour high-resolution automated confocal microscopy (Fig. 1a). targeting the same gene for a set of 78 genes and 468 siRNA (6 Instead of performing a phenotypic evaluation on the basis of single siRNAs per gene). We found that profiles of the same siRNAs were or few parameters as commonly done in previous screens10,11,we highly reproducible (Fig. 2a) (mean Pearson correlation coefficient developed a quantitative multiparametric image analysis (QMPIA) 0.6 6 0.02 s.e.m.), compared with other monoparametric screens13 1Max Planck Institute for Molecular Cell Biology and Genetics, 2High-Throughput Technology Development Studio, MPI-CBG, Pfotenhauerstrasse 108, 01307 Dresden, Germany. 3Center for Information Services and High Performance Computing (ZIH), Dresden University of Technology, D-01062 Dresden, Germany. 4Belozersky Institute of Physico-Chemical Biology, Moscow State University, 119899, Moscow, Russia. {Present addresses: University of Utah School of Medicine, 401 MREB, 20 North 1900 East, Salt Lake City, Utah 84132- 3401, USA (J.C.R.); Sirna Therapeutics Inc., San Francisco, California 94158, USA (D.K.). 243 ©2010 Macmillan Publishers Limited. All rights reserved | 11 March 2010 Other | Vol 464 NATURE TF runs P58 P57 { P56 { G10 P55 P52 { G9 C12orf4 P50 P47 P45 G8 P44 { P43 P42 G7 P41 { P40 Other P39 G6 EGF P38 { P37 P36 G5 P35 { P34 P33 G4 P32 { P31 P30 G3 { P29 { P28 G2 TF P26 a P23 { G1 P20 P18 G8 6.0 P17 { P16 P58 P15 G7 Parameter P57 { 4.0 P14 { all siRNAs P56 { G10 P13 P55 G6 P52 P12 { G9 2.0 P11 { P50 -values P10 P47 G8 z C12orf4 P45 P9 G5 { 0.0 P8 { P44 P7 P43 P6 P42 G7 G4 { –2.0 P5 { P41 P4 P40 P3 P39 G6 G3 { P2 { P38 Normalized Normalized –4.0 P1 EGF P37 G2 P36 G5 G1 P35 { –6.0 P34 P33 G4 P32 { P31 P30 G3 { P29 { P28 G2 P26 P23 { G1 Synthetic b P20 P18 G8 6.0 P17 { P16 P15 G7 Parameter 4.0 P14 { P13 P12 G6 gene. The same siRNA (ID: 28470) 2.0 P11 { , Reproducibility of -values P10 b z P9 G5 0.0 P8 { , Reproducibility of multiparametric profiles μm P7 P6 G4 C12orf4 –2.0 P5 { P4 P3 G3 b P2 { Normalized Normalized –4.0 P1 ARTICLES G2 –6.0 G1 EGF | Reproducibility of multiparametric profiles by the same siRNA a TF Other DAPI Control TFRC Other. endocyticBackground parameters intensity (2). Colocalization (2). G9 Figure 2 was silenced with six different siRNAs (IDs: 133085, G10 and different siRNAs/gene. a Quality control parameters of a single siRNA silencing the - Number of nuclei. μm 20 - Nuclear size. TF was transfected in sevenC12orf4 independent experiments (runs) and 20 - Nucleus intensity. multiparametric profiles). (differently coloured for each experiment) were - Focus score. calculated and plotted as described in Fig. 1. P58 P57 { multiparametric profiles of different siRNAs targeting the same gene 2 probability Cargo load P56 { G10 C12orf4 Cargo density P55 ( x P52 { G9 P50 133086, 133087, 28285, 28378, 28470) in the same experiment and P47 G8 Multi-parametric analysis P45 { corresponding multiparametric profiles were calculated and plotted as Internalizedcargo P44 c P43 Endosome. Number parameter of endosomes groups/cargo (1). G7 described in Fig. 1. P42 { G1 . Cargo uptake (2). P41 G2 . Endosome area (3). P40 P39 G6 G3 . Vesicle elongation (3). P38 { G4 . Endosomal cargo concentration (3). P37 probabilistic approach to infer gene-specific phenotypes by ‘averaging’ . Endosomal cargo content (3). P36 G5 G5 EGF { the profiles of different siRNAs targeting the same gene. We defined the . Endosome distance from nucleus (3). P35 G6 Number P34 12 , respectively) in the list of G7 . Endosome clustering (9). P33 G4 ‘gene profile’ as the most probable profile calculated as mode of the a { 2 G8 P32 P31 posteriori joint probability distribution considering all individual P30 G3 { { 10 P29 siRNA profiles for a given gene. Notably, here every siRNA targeting P28 G2 3 P26 { P23 G1 a given gene contributes, to a different extent, to the determination5 of P20 18 . Gene profiles instead of individual siRNA profiles were sub- G8 , Example of three-colour high- (Supplementary Table5 3) using seven siRNAs per d P18 { gene profiles (see Supplementary Fig. 4), circumventing the need of P17 19 P P16 P15 G7 Parameter similarity thresholds to define supporting siRNAs (2 of 3, 3 of 3, and 30 { P14 and P13 so on) 11 20 P12 G6 P11 { sequentlyAs test, used we for screened phenotypic 1,000 clustering genes2 including and gene classification. 35 known endocytic 10 P10 -value 0 P9 G5 z { 10 P8 15 , corresponding to P7 90%) and0.95 62 corresponding3 out of 92 kinase to genes a statistically presented significant a enrich- P6 G4 regulators (Supplementary Table 2) and 92 kinases identified in a –10 P5 { , $ P4 previous screen ) 1.1 –20 P3 G3 P P2 { gene. On the basis5 of the QMPIA, 31 of the 35 endocytic regulator Normalized Normalized –30 P1 G2 , Close-ups show the model structure of 2 P G1 b genes ( –40 ; see pictures in (1.0 ment ( | Multiparametric image analysis. a TFRC y-axis. Parameter groups scores. Interestingly, only 7 out of the 35 endocytic regulators would , List of quantitative parameters divided into groups have scored considering only two parameters (EGF and TF total c internal cargo).
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