An overview of the iPSC-RPE differentiation protocol used for optimization is shown in Figure 2A and Figure 1—figure supplement 1. It consists of five steps: seeding, preconditioning, passage, RPE differentiation (induction), and RPE maintenance culture. The day on which the passage was performed was defined as differentiation day (DDay) 0, and the cultured cells were sampled and validated on DDays 33 and 34. To implement this protocol using LabDroid, the necessary peripheral devices were installed on and around LabDroid’s workbench (Figure 2B, Figure 2—figure supplement 1). We designed the system to work simultaneously with eight 6-well plates per batch, for a total of 48 cell-containing wells. LabDroid was programmed for three types of operations: seeding, medium exchange, and passage (Figure 2—figure supplements 2–7; Figure 2—source data 3; Figure 2—video 1). The steps for the preconditioning and induction, which correspond to the preparation of reagents, were named medium exchange type I, and the step for RPE maintenance culture, which does not involve reagent preparation, was named medium exchange type II (Figure 2A, Figure 2—figure supplement 2).

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Figure 2—source data 1

Acquired pigmented images of the baseline experiment.

Images acquired on Day 34 of the baseline experiment; images of the bottom of the well with cultured cells, cropped to the size of the well. These 8-bit images were adjusted to a minimum and maximum contrast value of 100 and 150, respectively. IDs on the bottom indicate 'B (baseline) - Plate No. - Well No.'.

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Figure 2—source data 2

Executed parameters and scores of the baseline experiment.

Related to Figure 2E. Raw values of the parameter candidates and pigmentation scores in the baseline experiment. *KSR concentration was lowered in a systematic fashion, unlike the KP parameter. Specific values: DDays 1–3, 20% KSR; DDays 4–7, 15% KSR; from DDay 8, 10% KSR.

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Figure 2—source data 3

Pipetting volume and pipette combination.

Related to Figure 2. Given the limitations of the LabDroid, setting the micropipette to an arbitrary volume was difficult. Therefore, we pseudo-implemented a fine volume setting for the transfer of 0–1000 µL by combining nine micropipettes with pre-set volumes (3000, 1000, 450, 300, 200, 80, 30, 10, and 5 µL). The number of combinations was limited to three or fewer. The numbers in the table indicate the micropipettes and the number of times they had to be used to achieve the desired volumes. For example, 260 µL indicates that the 200 and 30 µL micropipettes had to be used once and twice, respectively.

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First, we used LabDroid to perform baseline experiments involving the induction of iPSC-RPE cell differentiation under the same conditions as the typical manual operations. Because of the differences in structure and experimental environment between the LabDroid and humans, some operations and movements, such as the use of a centrifuge, the presence or absence of cell counting at the time of passage, and the speed of movement, differed from those of humans. For example, achieving the same time interval for trypsin treatment in all wells of a single plate during cell detachment using LabDroid is difficult. Therefore, the passage operation was performed at six separate time intervals. The cells differentiating into RPE cells produce melanin, which causes them to turn brown. Therefore, the area ratio of the total number of pigmented cells on DDay 34 was used to estimate the differentiation induction efficiency and obtain evaluation scores, following the example of previous studies (Kuroda et al., 2019; Regent et al., 2019; Figure 2—figure supplement 8). These validation scores were used to simplify the validation process and do not reflect the entire quality of the RPE.

Baseline experiments were conducted and validated using six trypsin conditions and eight plates (Figure 2C–E; Figure 2—source data 1 and Figure 2—source data 2). The highest scoring was obtained when trypsin treatment was conducted for 20 min at 37 °C, followed by 14 min incubation at room temperature (RT, approximately 25 °C), with an eight-plate score of 0.44±0.03 (mean ± SEM, n=8). The lowest scoring was obtained when trypsin treatment was conducted for 20 min at 37 °C, followed by 23 min at RT, with an eight-plate score of 0.33±0.02 (mean ± SEM, n=8). LabDroid successfully performed the iPSC-RPE protocol, as evidenced by the detection of pigmented cells in all 48 wells and the lack of errors in the operating process. However, in the naive transplantation of the manual protocol to the robot, the induction efficiency was insufficient. This suggests that it is inherently difficult to describe physical parameters, including unrecorded human movements. Therefore, we attempted to optimize the protocol parameters to further improve the scores using a robotic search.

To improve the pigmentation score, we selected seven parameters for optimization: two from the preconditioning step, three from the passage step, and two from the induction step. Search domains were set for each parameter (Table 1; Figure 3A and B).

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From the preconditioning step on DDays −1 to −6, we selected two parameters for optimization: the concentration of fibroblast growth factor receptor inhibitor (FGFRi) in the medium (PC, preconditioning concentration), and the duration of addition (PP, preconditioning period). From the passage step performed on DDay 0, we selected three parameters to optimize: the pipetting strength during cell detachment (DS, detachment pipetting strength), the area of the bottom surface to be pipetted (DL, detachment pipetting length), and trypsin processing time (DP, detachment trypsin period) of a passage. DP is a contextual parameter that can only be used to perform experiments at fixed values, owing to the specifications of the experimental system. In this case, DP is allowed to take different fixed values at three-minute intervals, corresponding to the number of wells in the plate. From the induction step on DDays 1–25, we selected two parameters to optimize: the concentration of KnockOut Serum Replacement (KSR) in the medium (KP, KSR period), and the duration of exposure period of the three chemical supplements (3P, three supplement period).

To improve the optimization performance, 48 conditions (eight plates × six wells, as shown in Figure 2C) were executed in parallel in each batch. The 48 conditions were selected from the search space using the Bayesian optimization module to maximize the acquisition function calculated from the past experimental data. In general, solving a high-dimensional, expensive black-box optimization problem such as the present one with a limited number of rounds is challenging. In our case, some 200 million possible parameter combinations existed in the search space, and the point where the pigmented score was optimal in three rounds (144 queries) had to be determined, because one experiment round took 40–45 days. In recent studies, BBO has shown excellent performance in real-world black-box optimization problems (Burger et al., 2020; Gongora et al., 2020; HamediRad et al., 2019). We integrated an experimental design module based on BBO to effectively search for the optimal experimental parameters that maximize the pigmentation scores in the search space defined in Figure 3B.

The Bayesian optimization module generates queries using two components: the Model updater, which updates the surrogate model that captures the relationship between parameters and the scores using Bayesian inference (Figure 3—figure supplement 1); the Query generator, which generates the next experimental parameters ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ using an acquisition function and a policy function (Figure 3C, Figure 3—figure supplement 2; Algorithm 1–3). In the Query generator, the acquisition function estimates the expected progress toward the optimal experimental parameter at a given experimental parameter (Figure 3D). Then, using the acquisition function, the policy function generates the next 48 experimental parameters ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ considering the context of trypsin processing time ${\displaystyle x_{DP}}$ (Figure 3E).

To test the performance of the Bayesian optimization module in our case, we executed a preliminary performance validation using a toy testing function constructed on domain knowledge (Figure 3—figure supplements 3 and 4).

In this study, three successive experiments were conducted to optimize the target protocol. In each round, 48 conditions were generated using the Bayesian optimization module and translated into LabDroid operating programs. The robot performed 40 days of iPSC-RPE induction culture under each condition, and we obtained the rate of pigmented cells in the dish as an evaluation score (pigmentation score) for each condition. In accordance with the experimental design, we incorporated the two highest-scoring conditions from the previous experiment (Figure 2E) as control conditions, performed differentiation-inducing cultures with the LabDroid, and validated the area of the colored cells. In round 1, although one condition was found to be experimentally deficient, the other 47 conditions were validated. The highest score was 0.86 (Figure 4A; Figure 4—source data 1, Figure 4—source data 4), yielding five conditions that exceeded the mean value (0.39) for all wells in the baseline experiment (Figure 2E). In round 2, 46 conditions were generated, and the two highest-scoring conditions in round 1 were incorporated as control conditions. The highest score was 0.83 (Figure 4B; Figure 4—source data 2, Figure 4—source data 4). In round 3, 48 experiments were conducted, yielding an improved highest score of 0.91. We obtained 26 other conditions that were better than the highest in round 2 (Figure 4C; Figure 4—source data 3, Figure 4—source data 4). A visualization diagram of a two-dimensional partial least squares regression (PLS) clearly revealed that the overall experimental parameters tended to converge in a higher pigmented score direction from rounds 1 to 3 (Figure 4D, Figure 4—figure supplement 1).

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Figure 4—source data 1

Acquired pigmented images of the round 1 experiment.

Images acquired on Day 34 of the round 1 experiment; images of the bottom of the well with cultured cells, cropped to the size of the well. These 8-bit images were adjusted to a minimum and maximum contrast value of 100 and 150, respectively. ID labeling on the bottom indicates '1 (round 1) - Plate No. - Well No.'.

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Figure 4—source data 2

Acquired pigmented images of the round 2 experiment.

Images acquired on Day 34 of the round 2 experiment; images of the bottom of the well with cultured cells, cropped to the size of the well. These 8-bit images were adjusted to a minimum and maximum contrast value of 100 and 150, respectively. ID labeling on the bottom indicates '2 (round 2) - Plate No. - Well No.'.

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Figure 4—source data 3

Acquired pigmented images of the round 3 experiment.

Images acquired on Day 34 of the round 3 experiment: images of the bottom of the well with cultured cells, cropped to the size of the well. These 8-bit images were adjusted to a minimum and maximum contrast value of 100 and 150, respectively. ID labeling on the bottom indicates '3 (round 3) - Plate No. - Well No.'.

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Figure 4—source data 4

Executed parameters and scores of the optimization experiments.

Related to Figure 4. Raw values of the parameter candidates and pigmentation scores in the experiments from rounds 1 to 3.

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To determine whether the optimized conditions were statistically improved over the pre-optimized conditions, an additional multi-well validation experiment was conducted after round 3 using the top five conditions in round 3 and the pre-optimized conditions. The validation values, ordered by place, were 0.71±0.06, 0.72±0.03, 0.76±0.02, 0.79±0.02, and 0.81±0.02 (mean ± SEM, n=3 each). All scores after optimization were statistically significantly higher than the pre-optimization scores (0.43±0.02; mean ± SEM, n=3) (Figure 5A and B; Figure 5—source data 1, Figure 5—source data 2).

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Figure 5—source data 1

Acquired pigmented images of the validation experiment.

Images acquired on Day 34 of the validation experiment: images of the bottom of the well with cultured cells, cropped to the size of the well. These 8-bit images were adjusted to a minimum and maximum contrast value of 80 and 125, respectively. Sample names on the top correspond to Figure 5A. ID labeling on the bottom indicates 'V (validation) - Plate No. - Well No.'. Wells 2, 5, and 6 were not subjected to the validation experiments. Plates 1 and 5 were used for cell biological analysis and were not evaluated using images.

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Figure 5—source data 2

Executed parameters and scores of the validation experiment.

Related to Figure 4. Raw values of the parameter candidates and pigmentation scores in the validation experiments. The sample names on the top correspond to Figure 5A. Well numbers 2, 5, and 6 were not subjected to the validation experiments. *Plate numbers 1 and 5 were used for cell biological analysis and were not validated using images.

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Figure 5—source data 3

ELISA scores.

Related to Figure 5D and E. Raw values of ELISA scores from the validation experiments.

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Figure 5—source data 4

Immunohistochemistry images.

Related to Figure 5F. Images without contrast processing. TIFF files with three channels merged for each sample image: channel 1, MITF (magenta); channel 2, ZO-1 (green); channel 3, DAPI (gray).

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Figure 5—source data 5

Robot log.

List of job names, start times, end times, time required, number of commands, and errors (if any) for all experiments performed by the LabDroid in this study.

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In summary, we conducted 216 40-day cell culture experiments with a total experimentation time of 8640 days. We accelerated the search using a BBO technique, compressing the search time to 185 days with a cumulative robot operating time of 995 hr (Figure 5—source data 5; Figure 5—figure supplements 1 and 2; Figure 4—videos 1–5).

In this study, we succeeded in replacing part of the process of iPS cell differentiation into RPE cells for transplantation using robots, and demonstrated an effective optimization method (Figure 1—figure supplement 2). However, it was unclear whether robot-manufactured RPE cells would have the characteristics required for transplantation. Therefore, we purified the cells of the validation round, prepared them for transplantation, and performed a biological quality evaluation (Figure 1—figure supplement 1B). The analyzed iPSC-RPE cells expressed BEST1, RPE65, and CRALBP (Figure 5C), which are characteristic marker genes of RPE cells. In addition, we observed secretion of VEGF and PEDF into the culture medium, a characteristic of RPE cells (Figure 5D and E; Figure 5—source data 3). The expression of tight junction-associated factor ZO-1 was examined using immunohistochemistry, and a ZO-1-derived fluorescence signal was observed in microphthalmia-associated transcription factor (MITF)-positive cells, which play a central role in RPE cell function (Figure 5F). These results indicated that the robot-manufactured iPSC-RPE cells had the characteristics of RPE cells, and fulfilled the criteria for use in regenerative medicine research using the type of analysis measured in a previous clinical study (Mandai et al., 2017).

All experiments that involved the use of human-derived samples were reviewed and approved by the institutional review board of the Institutional Committee of RIKEN Kobe Branch (#Kobe1 2019–05 (3)).

hiPSC maintenance medium: 80% StemFit Basal Solution A and 20% StemFit iPS Expansion Solution B (#AK02N, Ajinomoto Co., Inc, Japan).

RPE differentiation medium (20% KSR): 0.10 mM MEM non-essential amino acids solution (NEAA) (#11140050, Thermo Fisher Scientific Inc, MA, USA), 1.0 mM sodium pyruvate (#S8636, Merck & Co., Inc, NJ, USA), 19% knockOut serum replacement (KSR) (#10828028, Thermo Fisher Scientific Inc, MA, USA), 0.0007% 2-mercaptoethanol (#139–06861, FUJIFILM Wako Pure Chemical Corporation, Japan), 78 U/mL benzylpenicillin sodium, and 78 µg/mL streptomycin sulfate (#15140122, Thermo Fisher Scientific Inc, MA, USA). All diluted in GMEM (#11710035, Thermo Fisher Scientific Inc, MA, USA).

RPE differentiation medium (15% KSR): 0.10 mM MEM NEAA (#11140050, Thermo Fisher Scientific Inc, MA, USA), 0.99 mM sodium pyruvate (#S8636, Merck & Co., Inc, NJ, USA), 15% KSR (#10828028, Thermo Fisher Scientific Inc, MA, USA), 0.0007% 2-mercaptoethanol (#139–06861, FUJIFILM Wako Pure Chemical Corporation, Japan), 82 U/mL benzylpenicillin sodium, and 82 µg/mL streptomycin sulfate (#15140122, Thermo Fisher Scientific Inc, MA, USA). All diluted in GMEM (#11710035, Thermo Fisher Scientific Inc, MA, USA).

RPE differentiation medium (10% KSR): 0.094 mM MEM NEAA (#11140050, Thermo Fisher Scientific Inc, MA, USA), 0.94 mM sodium pyruvate (#S8636, Merck & Co., Inc, NJ, USA), 10% KSR (#10828028, Thermo Fisher Scientific Inc, MA, USA), 0.0007% 2-mercaptoethanol (#139–06861, FUJIFILM Wako Pure Chemical Corporation, Japan), 85 U/mL benzylpenicillin sodium, and 85 µg/mL streptomycin sulfate (#15140122, Thermo Fisher Scientific Inc, MA, USA). All diluted in GMEM (#11710035, Thermo Fisher Scientific Inc, MA, USA).

RPE maintenance medium: 29% Nutrient Mixture F-12 (#N6658, Merck & Co., Inc, NJ, USA), 1.9 mM L-glutamine (#G7513, Merck & Co., Inc, NJ, USA), 1.9% B-27 supplement, serum free (#17504044, Thermo Fisher Scientific Inc, MA, USA), 96 U/mL benzylpenicillin sodium, and 96 µg/mL streptomycin sulfate (#15140122, Thermo Fisher Scientific Inc, MA, USA). All diluted in DMEM (Low glucose) (#D6046, Merck & Co., Inc, NJ, USA).

FGF receptor inhibitor (FGFRi) stock: PD 173074 (#P2499-5MG, Merck & Co., Inc, NJ, USA) diluted in DMSO (#D2650−5X5ML, Merck & Co., Inc, NJ, USA).

Rho-kinase inhibitor (Y) stock (8–10 mM): CultureSure Y-27632 (#036–24023, FUJIFILM Wako Pure Chemical Corporation, Japan) diluted in distilled water (Otsuka Pharmaceutical Factory, Japan) to a final 10 µM concentration when added to the cell culture medium.

TGF-β/Activin/Nodal signal inhibitor (SB) stock (4–5 mM): SB 431542 hydrate (#S4317-5MG, Merck & Co., Inc, NJ, USA) diluted in DMSO (#D2650−5X5ML, Merck & Co., Inc, NJ, USA) to a final 5 µM concentration when added to the cell culture medium.

Wnt signal inhibitor (CKI) stock (2.4–3 mM): CKI-7 dihydrochloride (#C0742-5MG, Merck & Co., Inc, NJ, USA) diluted in distilled water (Otsuka Pharmaceutical Factory, Japan) to a final 3 µM concentration when added to the cell culture medium.

RPE adhesion medium: DMEM/F12 (D8437, Merck & Co., Inc, NJ, USA), 10% FBS (12,007C, Nichirei Corporation, Japan).

RPE washing solution: 98% DMEM/F12 (D8437, Merck & Co., Inc, NJ, USA), 1 mM sodium pyruvate (S8636, Merck & Co., Inc, NJ, USA), 2 mM L-glutamine (G7513, Merck & Co., Inc, NJ, USA).

For human use: micropipette tip, 2140-05-HR/2149P-05/61849, Thermo Fisher Scientific Inc (MA, USA); micropipette tip, 30389165, Mettler Toledo (OH, USA); micropipette tip, 737251, Greiner Bio-One International GmbH (Germany); disposable pipette, 356507, Corning Incorporated (NY, USA); disposable pipette, 606160/607160/760160/768160, Greiner Bio-One International GmbH (Germany); filtration, SLGVJ13SL, Merck & Co., Inc (NJ, USA); filtration, SS-10LZ, Terumo Corporation (Japan); filtration, 431096/430281/431097/430282, Corning Incorporated (NY, USA); 1.5 mL tube, 72.692MS, Sarstedt K.K. (Japan); 15 mL tube, 352096, Corning Incorporated (NY, USA); 50 mL tube, 352070, Corning Incorporated (NY, USA).

For LabDroid use: 6-well plate, 353046, Corning Incorporated (NY, USA); 50 mL tube, MS-58500, Sumitomo Bakelite Co., Ltd. (Japan); micropipette tip, 3511-05-HR/3512-05-HR/94410313/94410713/94052550, Thermo Fisher Scientific Inc (MA, USA).

LabDroid including peripheral equipment were placed inside a booth made of acrylic walls and a stainless steel frame with three fan-filter-units (Figure 2—figure supplement 1). The LabDroid booth included a dual-arm humanoid (Robotic Biology Institute Inc, Japan), a CO₂ incubator (APC-30D, ASTEC Co., Ltd., Japan), micropipettes (4641110N/4641030N/4641230N/4641210N, Thermo Fisher Scientific Inc, MA, USA), a tube rack (Robotic Biology Institute Inc, Japan), a plate rack (Robotic Biology Institute Inc, Japan), a dry bath (EC-40RA, AS ONE Corporation, Japan), a tip sensor (Robotic Biology Institute Inc, Japan), an aspirator (SP-30, Air Liquide, Italy), a dust bin (EPD3S, Sekisui Techno Moulding Co., Ltd., Japan), and a microscope (EVOS FL Auto 2, Thermo Fisher Scientific Inc, MA, USA).

The hiPSC line 253G1 (Nakagawa et al., 2008), made from human dermal fibroblasts, was obtained from RIKEN BRC (HPS0002). The hiPSCs were cultured and differentiated using the method previously described (Haruta et al., 2004; Kawasaki et al., 2002; Osakada et al., 2008). Mycoplasma contamination tests were performed periodically during the study and the results were always negative.

On DDay −14, frozen hiPSCs were initiated using the following procedures: first, laminin-coated 6-well plates were prepared. A final concentration of 0.5 µg/cm² iMatrix-511 (Matrixome Inc, Japan) diluted in PBS (-) was then added to each well of the four 6-well plates and incubated for a minimum of 60 min at 37 °C and 5% CO₂, after which 0.75 mL/well of hiPSC maintenance medium was added. The supernatant was then removed. Next, 1 mL/well of hiPSC maintenance medium containing Rho-kinase inhibitor (final 10 µM concentration) was added, and the coated plates were incubated at 37 °C and 5% CO₂ until further use.

For hiPSC initiation, frozen vials of hiPSCs stored in liquid nitrogen were thawed in a water bath set at 37 °C, and the cells were subsequently suspended in 5 mL of hiPSC maintenance medium. After centrifugation (160×g, 22 °C, 4 min), the supernatant was removed and an appropriate volume of hiPSC maintenance medium with a final 10 µM Rho-kinase inhibitor concentration was added. After counting the cells with a hemocytometer, the cells were seeded into laminin-coated 6-well plates at 43,300–45,000 cells/1.5 mL medium/well.

On DDay −13, the medium was replaced with hiPSC maintenance medium without Rho-kinase inhibitor. On DDays −12 to −8, the medium was replaced with the same medium composition at 24–72 hr intervals. On DDay −7, cells were collected from the plate, and cell suspensions were delivered to the LabDroid booth. The medium was aspirated and 2 mL/well of PBS (-) was gently added and then aspirated for washing, followed by addition of 1 mL of 0.5 x TrypLE Select CTS (#A12859-01, Thermo Fisher Scientific Inc, MA, USA) diluted in 0.5 mM EDTA/PBS (-) and incubated at 37 °C and 5% CO₂ for 10–20 min. Then, cells were detached by pipetting and collected into a 50 mL tube, to which 1 mL of hiPSC maintenance medium and 3 mL of PBS (-) were added. After centrifugation (160×g, 22 °C, 4 min), the supernatant was removed, 0.75 mL of hiPSC maintenance medium with 10 µM Rho-kinase inhibitor was added, and the cells were resuspended. The cell suspension was filtered through a 40 µm cell strainer (#352340, Corning Incorporated, USA) with an additional 0.75 mL of hiPSC maintenance medium. After counting the cells with a hemocytometer, the cell suspension was set to 133,400 cells/20 mL with hiPSC maintenance medium containing 10 µM Rho-kinase inhibitor in eight 50 mL tubes. To prepare the cell suspensions, eight 6-well plates coated with laminin were prepared. A final concentration of 0.5 µg/cm² of iMatrix-511 (Matrixome Inc, Japan) diluted in PBS (-) was added to each well of four 6-well plates and incubated for a minimum of 60 min at 37 °C and 5% CO₂.

On DDay −7, the hiPSC suspension was seeded into eight 6-well plates by coating eight 6-well plates with laminin, and placing eight tubes of the iPSC suspension and labware in the appropriate positions. The task of seeding was initiated, and the robotic operation was performed by LabDroid (Figure 2—figure supplements 2A and 3; Figure 4—video 1). After the robotic operation, the eight cell-seeded plates were exported and incubated in a CO₂ incubator outside the LabDroid booth.

On DDay −6, the eight seeded plates were imported into the CO₂ incubator of the LabDroid booth. The users prepared eight 50 mL tubes of hiPSC maintenance medium with a final 10 µM Rho-kinase inhibitor concentration and two 50 mL tubes of hiPSC maintenance medium with final 5 µM FGFRi and 10 µM Rho-kinase inhibitor concentrations. The reagents and labware were placed in the appropriate positions. The task of preconditioning was then initiated, and the robotic operation was performed by LabDroid (medium exchange type I; Figure 2—figure supplements 2B and 4; Figure 4—video 2).

On DDays −5 to −1, the users prepared eight 50 mL tubes of hiPSC maintenance medium without Rho-kinase inhibitor and two 50 mL tubes of hiPSC maintenance medium with a final 5 µM FGFRi concentration. The reagents and labware were placed in the appropriate positions. The task of preconditioning was initiated, and the robotic operation was performed by LabDroid (medium exchange type I; Figure 2—figure supplements 2B and 4; Figure 4—video 2).

On DDay 0, the following procedure was used for the operation of four plates: the users prepared four 6-well plates coated with laminin. A final 0.5 µg/cm² concentration of iMatrix-511 (Matrixome Inc, Japan) diluted in PBS (-) was added to each well of the four 6-well plates and then the plates were incubated for a minimum of 60 min at 37 °C and 5% CO₂. The users also prepared two 50 mL tubes of PBS (-), two 50 mL tubes of 0.5 x TrypLE Select CTS (#A12859-01, Thermo Fisher Scientific Inc, MA, USA) diluted in 0.5 mM EDTA/PBS (-), and four plates with RPE differentiation medium (20% KSR) with final 10 µM Rho-kinase inhibitor/3 µM Wnt signal inhibitor/5 µM TGF-β/Activin/Nodal signal inhibitor (4 mL/well each) concentration. The cell plates, laminin-coated plates, plates with medium, reagents, and labware were placed in the appropriate positions. The task of passage was initiated, and robotic operations were performed by LabDroid (Figure 2—figure supplements 2D and 5; Figure 4—video 3). After performing this operation twice (four plates each), the eight cell-passaged plates were exported and incubated in a CO₂ incubator outside the LabDroid booth.

On DDay 1, the eight cell-passaged plates were imported into the CO₂ incubator of the LabDroid booth. Users prepared eight 50 mL tubes of RPE differentiation medium (10% KSR), two 50 mL tubes of 100% KSR, one 50 mL tube of 4 mM Rho-kinase inhibitor stock/1.2 mM Wnt signal inhibitor stock, and one 50 mL tube of 4 mM TGF-β/Activin/Nodal signal inhibitor stock. The reagents and labware were placed in the appropriate positions. The task of RPE differentiation was initiated, and the robotic operation was performed by LabDroid (medium exchange type I; Figure 2—figure supplements 2B and 6; Figure 4—video 4).

On DDays 2–19, the users prepared eight 50 mL tubes of RPE differentiation medium (10% KSR), two 50 mL tubes of 100% KSR, one 50 mL tube of 4 mM Rho-kinase inhibitor stock/1.2 mM Wnt signal inhibitor, and one 50 mL tube of 4 mM TGF-β/Activin/Nodal signal inhibitor. The reagents and labware were placed in the appropriate positions. The task of RPE differentiation was initiated, and the robotic operation was performed by LabDroid (medium exchange type I; Figure 2—figure supplements 2B and 6; Figure 4—video 4).

On DDays 20–32, the users prepared eight 50 mL tubes of RPE differentiation medium (10% KSR; DDays 10–25) or RPE maintenance medium (DDays 26–32). The reagents and labware were placed in the appropriate positions. RPE differentiation and maintenance were initiated and the robotic operations were performed by LabDroid (medium exchange type II; Figure 2—figure supplements 2C and 7; Figure 4—video 5).

On DDay 33, the cell plates were exported and the cell culture medium was replaced with fresh RPE maintenance medium. After 24 hr (DDay 34), the medium was collected for ELISA analysis. The remaining media were aspirated and 2 mL of PBS (-) were added and then aspirated for washing. After that, photographic images were acquired for the calculation of scoring values.

Images were acquired using a digital camera (PSG7X MARKII, Canon Inc, Japan): ISO 500; focal length F=9.00, 50 mm; exposure time, 1/1250 s. The camera was set in the same position throughout all experiments. The acquired images were automatically processed by filtering with Gaussian blur, subtracting the background, binarizing by thresholding with a constant value, and cropping with a constant pixel value. The colored cell area was then calculated (Figure 2—figure supplement 8).

Purification of iPSC-RPE cells was conducted using the same protocol described in a study previously reported (Mandai et al., 2017). When the RPE colonies reached an appropriate size, the cells were suspended in RPE maintenance medium and kept as a floating culture for about 10 days in a low cell adhesion plate (MS-90600Z, Sumitomo Bakelite Co., Ltd., Japan). Under the microscope, colonies consisting only of black RPE cells were selected. Then, they were transferred to 12-well plates coated with iMatrix, and cultured in RPE adhesion medium/RPE maintenance medium (1:1). Once the RPE cell colonies became attached to the dish, they were cultured in RPE maintenance medium with basic fibroblast growth factor (bFGF), which was changed every 2–3 days.

After 10–12 days of cell selection, unsuitable cells were removed, and the cells were passaged. The medium was aspirated and 1 mL of RPE washing solution was added and aspirated again for washing. Then, 0.5 mL of RPE washing solution was added and atypical cells were eliminated using micropipette tips under microscope observation. After the removal process, the medium was aspirated, 1 mL/well of PBS (-) was added and aspirated for washing, and then 0.5 mL of Trypsin-EDTA solution (203–20251, FUJIFILM Wako Pure Chemical Corporation, Japan) was added, followed by incubation at RT and 5% CO₂ for 8–10 min. Cells were detached by pipetting and collected into a 50 mL tube. After centrifugation (280×g, 25 °C, 4 min), the supernatant was removed, and the pellet was resuspended in 1 mL/plate of RPE adhesion medium/RPE maintenance medium (1:1) and filtered through a 40 μm cell strainer (352340, Corning Incorporated, NY, U.S.A.). After counting the cells with a hemocytometer, the cells were seeded into 12-well plates. The medium was changed to RPE maintenance medium with bFGF.

After 1–3 days of cell passage, the medium was aspirated, the cells were washed with 0.5 mL of RPE maintenance medium, and 1 mL of RPE maintenance medium containing 10 ng/mL bFGF and 0.5 µM SB431542 was added. This medium was exchanged every 2–3 days.

The cells were stored when they formed hexagonal shapes after sufficient confluency. For that, the medium was aspirated, 1 mL/well of PBS (-) was added and then aspirated for washing, and 0.5 mL of Trypsin-EDTA solution (203–20251, FUJIFILM Wako Pure Chemical Corporation, Japan) was added, followed by incubation at 37 °C and 5% CO₂ for 10–15 min. After adding >0.5 mL of RPE adhesion medium, the cells were detached using a cell scraper (MS-93100, Sumitomo Bakelite Co., Ltd., Japan). The cell suspension was filtered through a 40 μm cell strainer (352340, Corning Incorporated, NY, USA) and then centrifuged for 4 min at 280×g to obtain a cell pellet. The pellet was resuspended in 1 mL of RPE adhesion medium/RPE maintenance medium (1:1) and filtered through a 40 μm cell strainer. After counting the cells with a hemocytometer, the cell suspension was centrifuged for 4 min at 280×g to obtain a cell pellet. Then, STEM-CELLBANKER (CB047, Zenoaq Resource Co., Ltd., Japan) was added until a cell concentration of 500,000 cells/0.5 mL/tube, and the cell suspensions were dispensed into cryovials. The cryotubes were placed in a cell freezing container at −80 °C for 3–24 hr, and then stored at −150 °C.

Frozen vials of RPE cells were thawed in a 37 °C water bath and suspended in 4.5 mL of RPE adhesion medium. After centrifugation (280×g, 25 °C, 4 min), the supernatant was removed and RPE adhesion medium/RPE maintenance medium (1:1) was added. After counting the cells with a hemocytometer, the cells were seeded into 24-well plates (0.5 mL/well).

After 1–3 days of cell seeding, the medium was aspirated, the cells were washed with 0.25 mL of RPE maintenance medium, and 0.5 mL/well of RPE maintenance medium containing 10 ng/mL bFGF and 0.5 µM SB431542 was added. This same type of medium was exchanged every 2–3 days.

Two weeks after seeding, the RPE cells were passaged. Two weeks after cell passage, the RPE cells were used for cell biological validation processes (RT-PCR, ELISA, and immunohistochemistry).

Total RNA was extracted from transfected cells using RNeasy Micro Kit (#74004, QIAGEN, Germany). First-strand cDNA synthesis was performed on 500–1000 ng of total RNA, using SuperScript III (#18080–044, Thermo Fisher Scientific Inc, MA, USA) according to the manufacturer’s instructions. Each mRNA transcript was amplified using PCR with the following primers:

• BEST1 (+), 5′-dTAGAACCATCAGCGCCGTC

• BEST1 (−), 5′-dTGAGTGTAGTGTGTATGTTGG

• RPE65 (+), 5′-dTCCCCAATACAACTGCCACT

• RPE65 (−), 5′-dCCTTGGCATTCAGAATCAGG

• CRALBP (+), 5′-dGAGGGTGCAAGAGAAGGACA

• CRALBP (−), 5′-dTGCAGAAGCCATTGATTTGA

• GAPDH (+), 5′-dACCACAGTCCATGCCATCAC

• GAPDH (−), 5′-dTCCACCACCCTGTTGCTGTA

The collected media were centrifuged (90×g, 4 °C, 1 min), and the supernatant was collected and stored at −80 °C. The amount of VEGF contained in the thawed medium was measured using the protocols and reagents from the VEGF Human ELISA Kit (BMS277-2, Thermo Fisher Scientific, USA), and the amounts of PEDF were measured using a Human ELISA Kit (RD191114200R, BioVendor, Czech Republic).

Cells were washed with PBS (-), fixed in 15% paraformaldehyde for 1 hr at RT (approximately 25 °C), and stored at 4 °C after removal of PFA and addition of PBS (-). After removal of the solutions, cells were treated with 50 µL/well of 0.2% Triton X-100/PBS (-), incubated for 30 min at RT, washed with PBS (-), blocked with 50 µL of Blocking One (03953–95, Nacalai Tesque Inc, Japan), and incubated for 1 h at RT. After removal of the solutions, cells were stained at 4 °C o/n in 50 µL of the 1st antibody diluent (rabbit anti-ZO-1, 61–7300, Thermo Fisher Scientific Inc, MA, USA; anti-MITF, mouse anti-MiTF, ab80651, Abcam plc., Britain; antibody diluent, S2022, Agilent Technologies Inc, USA). After removal of the solutions, cells were washed with PBS (-) and then stained at RT for 1 hr in 50 µL of the 2nd antibody diluent (Alexa Fluor 546 Goat Anti-mouse IgG, A-11030, Thermo Fisher Scientific Inc, MA, USA; Alexa Fluor 488 Goat Anti-rabbit IgG, A-11034, Thermo Fisher Scientific Inc, MA, USA; antibody diluent, S2022, Agilent Technologies Inc, USA) with DAPI (1 µg/mL, D1206, Thermo Fisher Scientific Inc, MA, USA). After removal of the solutions, cells were washed with PBS (-), and then 50 µL of PBS (-) was added. Images of immunohistochemistry samples were acquired using an IX73 inverted microscope (Olympus, Japan).

When no prior experimental results exist, the Bayesian optimization module generates the next query from random uniform sampling. When past experimental results are available, the Bayesian optimization module generates queries using two components: the Model updater and the Query generator (Figure 3C).

The Model updater updates the surrogate model to predict the experimental results given past experimental results: ${\displaystyle \mathcal{D}={\left\{({\mathit{x}}_i,y_i)\right\}}_{i=1}^n}$. We adopted Gaussian process regression (GPR, Figure 3—figure supplement 1) with the ARD-RBF kernel as the surrogate model to estimate the expected score and confidence level for all unevaluated experimental parameters. Based on the experimental results shown in Figure 2E, the observation noise was assumed to follow a zero-mean Gaussian noise with a variance of 0.0039 at all points in the search space. By using the surrogate model, the Query generator generates the next queries in two steps. In step 1, the Query generator constructs an acquisition function that estimates the expected progress toward the optimal experimental parameter at a given experimental parameter ${\displaystyle x}$ in the search space. We adopted the Expected improvement (EI) (Jones et al., 1998), a commonly used acquisition function in BO. EI estimates how much improvement over the current best score is expected from each point in the search space. In step 2, by using the acquisition function, the Query generator decides where to evaluate next, and our problem required the simultaneous performance of 48 experiments corresponding to 8 plates x 6 wells in each round. In addition, because the DP is a batch contextual parameter as described herein, a policy function that generates parameter sets taking such structural context into account must be incorporated. Therefore, we developed the Batch Contextual Local Penalization (BCLP) as a policy function to generate multiple points with context in parallel. The BCLP is a batch generation policy that extends the local penalization (Gonzalez et al., 2016) to be applied to cases where complex structural context parameters exist. As shown in Figure 3E, for each value of the contextual parameter DP in ascending order, BCLP iteratively generated the parameter by maximizing and penalizing the acquisition function 48 times to obtain the next experimental parameters ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ for each subsequent well (Algorithm 1, 2). In addition, after each round, the more promising KP intervals were reconfigured by calculating the integral value of the acquisition function (Algorithm 3). We also replaced the queries that corresponded to the place of the top two pigmentation scores in the previous experiments with the parameter of the top two pigmentation scores in the previous experiments as a positive control. For more information about the optimization module, see the Appendix.

Statistical analyses were performed by Wolfram Mathematica version 11.2.0.0. In this study, p<0.05 was considered significant (*p<0.05, **p<0.01, ***p<0.001, and n.s.=not significant).

All code that supports the findings of this study is available at https://github.com/labauto/LabDroid_optimizer, (copy archived at swh:1:rev:661ef792d4b7568a2e673178d9f1e6ed3c84ab1b, Tsuzuki, 2022). This code is based on GPyOpt (GPyOpt: Gaussian Process Optimization using GPy).

The addition of FGFRi to the medium during preconditioning significantly increased the efficiency of induction of differentiation into RPE cells (Kuroda et al., 2019). The unit of PC is nM, and it takes values from 0 to 505, while PP is the number of days FGFRi is added, taking values from 1 to 6. For example, PC=300 and PP=2 indicates that 300 nM of FGFRi is added to DDays –2 and –1, and not from DDays –6 to –3.

DS was adopted because shear stress affects cell properties (Wall and Davie, 2013), and DL was adopted because the number of cells at passage is empirically known to contribute to the efficiency of differentiation induction. DS takes integer values from 10 to 100, indicating the speed at which the micropipette syringe is pressed (unit: mm/sec); DL takes two integer values, one long and one short, indicating the bottom surface area to be pipetted. We adopted DP at passage because of the constraints in the configuration of the LabDroid, which required different fixed values. DP takes integer values of 5, 8, 11, 14, 17, 20, and 23 to indicate the number of minutes that the trypsin solution was added and incubated at room temperature after incubation at 37 °C for 20 min.

The three chemical supplements are Rho-kinase inhibitor, TGF-β/Activin/Nodal signal inhibitor, and Wnt signal inhibitor, all of which are known to affect the culture and differentiation of iPS cells (Osakada et al., 2009; Watanabe et al., 2007). KSR was added to the medium at a 20% concentration on DDay 0 and was progressively reduced to 10%. Under baseline conditions, KSR is added in the following manner: 20% on DDays 0–4, 15% on DDays 5–7, and 10% from DDay 8, suggesting that this rate of reduction is important for the induction of RPE differentiation (Kuroda et al., 2019). The KP takes values from 1 to 19 and the concentration of KSR is decreased linearly daily until KSR becomes 10% on the DDay of the KP value. For example, when KP=4, KSR will be 20% on DDay 0, 17.5% on DDay 1, 15.0% on DDay 2, 12.5% on DDay 3, and 10% on DDay 4. 3P takes integer values from 3 to 19, and indicates the day in which the three chemical supplements are included in the differentiation-inducing medium from DDay 0 to the DDay of that 3P value.

In Bayesian optimization, the Gaussian process (GP) ${\displaystyle p(f)=\mathcal{G}\mathcal{P}(\mu ,\sigma ;k)}$ is typically used as the surrogate model to approximate the I/O of the objective function $f$. The GP is characterized by three components: a mean function µ, a variance function $\sigma$ and a kernel function (positive-value covariance function) $k$. In case of past experimental data ${\displaystyle \mathcal{D}={\left\{({\mathit{x}}_i,y_i)\right\}}_{i=1}^n}$ of $n$ observations, and Gaussian prior ${\displaystyle \mathcal{G}\mathcal{P}(0,\sigma ;k)}$, Gaussian Posterior is then given in ${\displaystyle \mathcal{G}\mathcal{P}({\mu }_n,{\sigma }_n;k)}$, where

${\displaystyle {\mu }_n(\mathit{x})}$is the mean of Gaussian process posterior function, and ${\displaystyle {\sigma }_n^2(\mathit{x})}$ is the variance of Gaussian process posterior function. In this work, we assume that the kernel function $k$ is stationary. ${\sigma }^2$ is the observation noise of the experiment.

Figure 3—figure supplement 1 shows an example of a Gaussian process prior to being updated to a Gaussian posterior using observations. The uncertainty of GP distribution decreases around the observed points and increases further away from observations. For a more comprehensive explanation of Gaussian process regression, see Rasmussen and Williams, 2006.

Bayesian optimization is a common black-box optimization method used to determine the input parameter ${\displaystyle x}$, which maximizes the objective function ${\displaystyle f}$ with as few executions as possible using iterative experiment-observation loops.

Figure 3—figure supplement 2 shows a toy example of how sequential Bayesian optimization generates the next experimental parameter. Bayesian optimization iteratively generates the following experimental parameters in three steps: construction of a surrogate model (e.g., Gaussian process regression) for past experimental results (left column), definition of an acquisition function ${\displaystyle \alpha (\mathit{x};\mathcal{D})}$ (e.g., expected Improvement), and optimization of the acquisition function to obtain the next experimental parameter (right column). For a more comprehensive explanation and application of Bayesian optimization, see Frazier and Wang, 2015 and Shahriari et al., 2016.

In the batch Bayesian optimization for the iPSC-RPE differentiation protocol, we generated the next experimental queries for each round in four steps (Algorithm 1). First, we constructed a Gaussian process posterior using a past experimental dataset. When no prior experimental results existed, we generated the next query from random uniform sampling. Next, using the Gaussian process posterior, we obtained an acquisition function. In this study, we used the expected improvement (Jones et al., 1998) as the acquisition function. Third, using the acquisition function, we generated a next experimental parameter set ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ using a policy function. In this study, we used batch contextual local penalization (BCLP) as the batch generation policy. Finally, after executing experiments on the parameter set ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$, we computed the optimal context for the Detach trypsin Period $x_{DP}$ for the next round.

In Gaussian process regression, we assume the ARD-RBF kernel function $k$ as follows:

${\displaystyle \mathit{\theta }=\{l_m{\}}^d}$represents d-dimensional length scale hyper-parameters that were optimized using the maximum-likelihood estimation in every GP fitting.

Based on the experimental results in Figure 2E, the observation noise was assumed to follow a normal distribution with a variance of 0.039, at all points in the search space.

In this work, we used the expected improvement as an acquisition function. The expected improvement estimates how much improvement over the current best score ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ is expected from each one of the input parameters ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ in the search space, as shown in the following form:

${\displaystyle \mathcal{D}}$is the set of the past experimental results. ${\displaystyle \mathrm{\Phi }}$ is the standard normal cumulative distribution function, and ${\displaystyle \varphi }$ is the standard normal probability density function.

In the policy function (see Figure 3E), BCLP iteratively generated the parameter for each value of the contextual parameter DP in ascending order by maximizing and penalizing the acquisition function 48 times to obtain the next experimental parameters ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$. Here we show the algorithm that generates ${\displaystyle {\mathit{X}}_{\mathrm{n}\mathrm{e}\mathrm{x}\mathrm{t}}}$ (Figure 3E). Starting from plate1, well1, BCLP fixes the value of the DP corresponding to a given well number and then maximizes the acquisition function to generate an experimental parameter for the well. The penalization of the acquisition function is then performed using a hammer function to the point where the experimental parameter is generated. Similarly, for the next well, an experimental parameter is generated by maximizing the penalized acquisition function and is penalized using the hammer function. By repeating such maximization-penalization loops, BCLP generates the next experimental parameters for each subsequent well.

In Algorithm 2, the function ${\displaystyle \phi (\mathit{x};{\mathit{x}}_j)}$ is a local penalizer of ${\displaystyle \alpha (\mathit{x};\mathcal{D})}$ at ${\displaystyle {\mathit{x}}_j}$ such that:

where

for ${\displaystyle erfc}$ the complementary error function, ${\displaystyle \hat{M}={\mathrm{m}\mathrm{a}\mathrm{x}}_i\left\{y_i\right\}}$ is the best score in the past experiments and ${\displaystyle \hat{L}={\mathrm{m}\mathrm{a}\mathrm{x}}_{\chi }\Vert {\mu }_{\mathrm{\nabla }}(\mathit{x})\Vert }$ is an approximated Lipschitz constant. Both ${\displaystyle \hat{M}}$ and ${\displaystyle \hat{L}}$ were calculated in each round. We used ${\displaystyle g(z)=z}$ when the acquisition function ${\displaystyle \alpha (\mathit{x};\mathcal{D})}$ was positive and the ${\displaystyle g(z)=softplus(z)}$ elsewhere (Gonzalez et al., 2016).

For the contextual parameter ${\displaystyle x_{DP}}$, contexts on a fixed trypsin processing time ${\displaystyle c_{DP,t,i}}$ are assigned for each well depending on the round number ${\displaystyle t}$ and the well number ${\displaystyle i}$, because of the implementation constraints of the protocol. After each round, the context on the ${\displaystyle x_{DP}}$ is reconfigured as a variable. In the initial state (round ${\displaystyle t=1}$), trypsin treatment time was assigned to the smallest well number (well 1) for eight minutes, and then it increased by three minutes for each additional well number.

In the next round, the context on ${\displaystyle x_{DP}}$ was allowed to move back and forth ${\displaystyle \mathrm{\Delta }c=3}$ in parallel while maintaining a three minute interval processing time between the different wells. Thus, the context in round 2 ${\displaystyle {\mathit{c}}_{DP,2}}$ will be ${\displaystyle {\mathit{c}}_{DP}}$ or ${\displaystyle {\mathit{c}}_{DP}^{-}}$ or ${\displaystyle {\mathit{c}}_{DP}^{+}}$ as follows:

Using the flow shown in Algorithm 3 in each round, we could choose whether the context in the next round ${\displaystyle {\mathit{c}}_{DP,t+1}}$ will be ${\displaystyle {\mathit{c}}_{DP}}$, ${\displaystyle {\mathit{c}}_{DP}^{-}}$ or ${\displaystyle {\mathit{c}}_{DP}^{+}}$, based on the past experimental data.

We performed a regression with GPR in each round and defined the acquisition function EI based on the regressions, deriving the integral value of the EI for ${\displaystyle x_{DP}}$ for each different candidate interval of ${\displaystyle x_{DP}}$ (either at 5 min, 8 min, or 11 min start), respectively. When any derived integrals were improved by 5% or more compared to the integrals in the interval used in the current round, the interval for the DP for the next round was run in the interval that showed the largest integral value among the candidate intervals.

Bayesian optimization was tested by optimizing the seven dimensional toy testing function shown in Figure 3—figure supplement 3 under different conditions. Figure 3—figure supplement 4A shows a comparison between the performance of batch Bayesian optimization and that of random search when the scale of the observation noise on the testing function was changed. The testing function with different noise (SD=0.000, 0.064, 0.400) was optimized using batch Bayesian optimization with batch contextual local penalization (BCLP) and uniform random sampling, using 8 plates × 6 wells=48 queries per round. In each condition, we performed 18 independent experiments. When the observation noise was not present (SD=0.000) or was sufficiently small (SD=0.064), as in the proposed system shown in Figure 2E, BCLP shows better convergence performance compared to the random search. When the scale of the observation noise was relatively large (SD=0.400), the performance of BCLP was only as good as that of the random search case.

Figure 3—figure supplement 4B compares the performances of BCLP when the batch size was changed. For the benchmark function (noise SD=0.064), optimization was performed using a different number of plates (Np) per round. In each condition, we performed n=18 independent experiments and showed that the convergence performance improved as the number of plates (the batch size) increased.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank T Iwata, T Mitsuyama, H Ozaki, and N Yachie for their insightful comments; H Hirabayashi and E Takagi for research support; A Kato and H Uchida for illustrations; and J Freeman for carefully proofreading the manuscript. We also thank all the laboratory members at RIKEN BDR, in particular, N Koide, Y Shibata, A Maeda, and T Maeda for their kind help in the preparation of the materials, their support in the experiments, and their insightful discussions.

Human subjects: All experiments that involved the use of human-derived samples were reviewed and approved by the institutional review board of the Institutional Committee of RIKEN Kobe Branch (#Kobe1 2019-05 (3)).

1. Preprint posted: November 27, 2020 (view preprint)
2. Received: January 12, 2022
3. Accepted: May 17, 2022
4. Version of Record published: June 28, 2022 (version 1)

© 2022, Kanda, Tsuzuki et al.

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