Transport of Miro1/TRAK by kinesin-1 is not affected by Ca2+. Instead, we demonstrate that the microtubule docking protein syntaphilin induces resistive forces that stall kinesin-1 and dynein-driven motility. Our results suggest that mitochondrial transport stalls by Ca2+-mediated recruitment of syntaphilin to the mitochondrial membrane, not by disruption of the transport machinery.Mitochondrial transport along microtubules is mediated by Miro1 and TRAK adaptors that recruit kinesin-1 and dynein-dynactin. To understand how these opposing motors are regulated during mitochondrial transport, we reconstitute the bidirectional transport of Miro1/TRAK along microtubules in vitro. We show that the coiled-coil domain of TRAK activates dynein-dynactin and enhances the motility of kinesin-1 activated by its cofactor MAP7. We find that TRAK adaptors that recruit both motors move towards kinesin-1’s direction, whereas kinesin-1 is excluded from binding TRAK transported by dynein-dynactin, avoiding motor tug-of-war. We also test the predictions of the models that explain how mitochondrial transport stalls in regions with elevated Ca The mechanisms of microtubule-based mitochondrial transport remain poorly understood. Here, the authors show that the mitochondrial TRAK adaptors activate the dynein-dynactin complex, enhance the motility of kinesin, and can scaffold both motors to control bidirectional transport. In neurons, mitochondria are distributed to distal regions where ATP and Ca2+ buffering are in high demand, such as synapses and axonal branches1. Mitochondrial transport is essential for axonal growth and branching, maintaining action potentials, and supporting synaptic transmission1. Aged and dysfunctional mitochondria need to be transported back to the cell body for degradation2. Defects in mitochondrial transport are associated with a variety of neurodegenerative disorders, including Parkinson’s disease1.Mitochondria are cellular power plants that generate most of the ATP needed for many biochemical reactions and have a high capacity to buffer cytosolic Ca3. Miro1 is composed of two GTPase domains and two EF-hands that bind Ca2+. Miro1 is localized to the outer mitochondrial membrane through its transmembrane domain5, and is coupled to the C-terminus of the trafficking of kinesin-binding (TRAK) adaptors6. The N-terminal coiled-coil domain of TRAK1 and TRAK2 recruit kinesin-1 and dynein-dynactin7, which transport mitochondria towards the plus- and minus-ends of microtubules (MTs), respectively9. Co-immunoprecipitation studies showed that TRAK1 recruits both dynein and kinesin whereas TRAK2 primarily interacts with dynein7. TRAK1 was found to be enriched in the axons of cultured neurons, while TRAK2 was found mainly in dendrites7, suggesting that these adaptors have nonredundant roles in mitochondrial trafficking.Early genetic screens identified the mitochondrial Rho GTPases Miro1 and Miro2 as essential factors regulating mitochondrial transport and quality control13. The complex transport properties of mitochondria are primarily driven by Miro1, TRAK adaptors, motors, and other associated factors1, but little is known about how these components control directionality and pausing of mitochondria. In vitro reconstitution studies showed that TRAK1 binds and activates kinesin motility14. TRAK2 was also shown to recruit both kinesin and dynein and regulate their activity in cell extracts15, but the underlying mechanism of how TRAK facilitates the activation and coordination of these opposing motors and mediates the transport of Miro1 is not well understood.Live cell imaging studies showed that mitochondria exhibit rapid anterograde and retrograde transport, interspersed with infrequent pausing and directional switching in axons and dendrites1. Studies in cultured neurons demonstrated that mitochondrial transport stalls when local Ca2+ concentrations are elevated8. Yet a rise in the cytosolic Ca2+ concentration fails to halt mitochondrial transport when an EF-hand mutant of Miro1 is expressed in neurons and other cell types17, indicating that Miro is the Ca2+ sensor that facilitates this process. However, the mechanism by which Ca2+ binding to Miro1 prevents motors from driving mitochondrial transport remains controversial18. The ‘motor detachment’ model proposes that Ca2+ binding to Miro1 decouples kinesin from TRAK5. In the ‘Miro-binding’ model, Ca2+ binding causes Miro1 to directly interact with the kinesin motor domain and inhibits its motility17. These models do not explain how elevated Ca2+ concentration also stops the retrograde mitochondrial transport driven by dynein. In addition, the knockdown of both Miro1 and Miro2 does not completely suppress Ca2+-induced arrest of mitochondria in neurons19, suggesting that the Ca2+-mediated arrest may function independently of the transport machinery. Recent studies in mouse models proposed an alternative model, in which a mitochondrial docking protein, syntaphilin (SNPH) anchors mitochondria to MTs in axons18. SNPH is recruited to mitochondria in response to sustained neuronal activity and elevated Ca2+ levels18. This model is supported by the observations that overexpression of SNPH completely abolishes mitochondrial transport in both directions and that increasing cytosolic Ca2+ fails to arrest mitochondrial transport in axons of SNPH knock-out neurons20. The “engine-switch and brake” model proposes that SNPH inhibits kinesin through direct molecular interactions and serves as a brake by anchoring mitochondria to MTs20. The predictions of these models could not be directly tested in vitro due to the lack of reconstituted assays from purified components.In mature neurons, two-thirds of mitochondria are docked to MTs2+-mediated stalling of mitochondrial transport. Miro1 stably interacts with kinesin/TRAK, and the motility of this complex is unaffected by excess Ca2+. However, static anchoring by SNPH is sufficient to stall kinesin or dynein motility. These results provide insight into the regulation of mitochondrial transport.In this study, we investigated the role of Miro1 and TRAK in the recruitment and activation of dynein and kinesin motility using in vitro reconstitution. We show that TRAK1 and TRAK2 activate dynein-dynactin motility. TRAK1 also increases the kinesin landing rate onto MTs but the MT-associated protein MAP7 is required to stimulate robust kinesin motility. TRAK1 or TRAK2 can simultaneously recruit dynein-dynactin and kinesin and these complexes are exclusively transported to the plus-end of MTs by kinesin. In comparison, kinesin does not colocalize to the TRAK adaptors transported to the minus-end by dynein/dynactin, demonstrating that TRAK coordinates the activity of opposing motors to avoid futile tug-of-war. We also distinguished between the predictions of the existing models of Ca22. Sequence alignments with established dynein adaptors confirmed that TRAK1 and TRAK2 contain the CC1 box that binds the dynein light-intermediate chain (LIC)23 and the Spindly motif that interacts with the pointed-end of dynactin24  led to robust activation of dynein-dynactin motility towards the MT minus-end , and -TRAK21–400 (DDT21–400) complexes  were comparable to that of dynein-dynactin assembled with BicD adaptors in vitro27 and the retrograde transport speed of mitochondria (300–900 nm s−1) in vivo12  resulted in only occasional motility  binds to TRAK11–360, and to a lesser extent TRAK21–360  complexes on MTs  of full-length TRAK1, but we observed little to no increase in kinesin landing rate by the addition of 20-100 nM TRAK11–360, suggesting that TRAK alone is insufficient to trigger robust activation of kinesin motility  about 1.5-fold compared to the no TRAK condition, while we did not observe a significant increase in the presence of 5 nM TRAK21–360  and full-length (TRAK11–953 and TRAK21–914) TRAK constructs with kinesin on MTs   Fig. . Collect0%) Fig. .14. We also observed MT binding of 150 nM TRAK11–360 and a TRAK1 construct containing the coiled coils and part of the C-terminal domain (TRAK11–532) in the absence of kinesin. However, the affinity of TRAK for MTs was weak and we could not detect MT binding of TRAK11–360, TRAK11–532, or full-length TRAK1 when TRAK concentration was lowered to 20 nM  at low TRAK concentrations  colocalizers moving along the MT . If the direction of DDKT is determined by mechanical competition, we expected DDKT11–400 complexes assembled with KIF5BΔ1–336 to move towards the minus end. However, the addition of excess KIF5BΔ1–336 only resulted in more than a 5-fold reduction in DDT11–400 landing rate  or full-length TRAK constructs  co-precipitated with Miro1  complexes that walk along the MTs  were similar to DDT11–532 only  or excess (2 mM) Ca2+  of TRAK1 was sufficient to co-precipitate with kinesin7. Based on the structures of dynein-dynactin assembled with BicD or Hook family adaptors48, this entire region is expected to run from the pointed to the barbed end of dynactin in active DDT complexes. If kinesin has a smaller footprint on TRAK, it may block some of the pairwise interactions required for the formation of the active DDT complex, but not fully inhibit the binding of dynein and dynactin to the rest of this coiled-coil. In comparison, activation of the DDT complex may block the entire kinesin binding site, and thereby, exclude kinesin from motile DDT complexes. Alternatively, TRAK may coordinate motor binding through a registry shift of its coiled coils, as proposed for the BicD2 adaptor49. These possibilities could be best distinguished by future cryo-electron imaging of reconstituted DDKT complexes at near-atomic resolution.The motility of DDKT complexes was markedly different from the reconstituted kinesin-3/Hook3/dynein-dynactin complex2+ arrest mitochondrial transport. We first tested whether Miro1 dissociates from or inhibits the KT complexes in the presence of excess Ca2+. The assembly and motility of the KTM complexes were unaffected by increased Ca2+ levels, which is incompatible with both ‘motor detachment’ and ‘Miro binding’ models17. Our results indicate that Ca2+ binding to Miro1 does not directly disrupt the machinery that transports mitochondria, and thereby Ca2+-mediated docking of mitochondria might occur upstream of the motor transport machinery 50, Armadillo repeat-containing X-linked (Armcx) 1 and 352, and mitochondrial fusion proteins MFN1 and MFN2 have been shown to interact with the Miro1/TRAK complex, and knockdown of these factors led to defects in mitochondrial transport46. The in vitro reconstitution assay we developed provides an experimental platform to investigate the molecular mechanism of how these factors regulate mitochondrial transport.We note that our results do not discount the role of Miro1 to coordinate the arrest of mitochondrial transport. Miro1 may serve as the primary factor that facilitates CaKIF5B; amino acids 1–963, clone ID 8991995) was obtained from GE Dharmacon and fused to GFP-SNAPf at its C terminus. The phi mutant of the dynein-1 heavy chain (DHC) was co-expressed by fusing the coding sequence to the pDyn2 plasmid containing genes encoding IC2C, LIC2, TCTEX1, LC8, and ROBL1, as described26. The list of constructs used for each dataset is given in Supplementary Table The constructs expressing the phi-dynein mutant (SNAP–DHC R1567E-K1610E), full-length TRAK1, and full-length TRAK2 were provided in a pACEBac1 vector backbone by A.P. Carter . The sequences encoding full-length or truncated versions of human TRAK1 and TRAK2 were cloned into the pOmniBac vector. All constructs contained an N-terminal His6-ZZ tag followed by a TEV protease cleavage site for protein purification and a C-terminal SNAP-tag or GFP fusion for labeling and imaging purposes. A cDNA for full-length human kinesin  and frozen in liquid nitrogen. Three frozen brains were broken into pieces and blended in a blender in the presence of homogenization buffer supplemented with four EDTA protease-inhibitor tablets per 500 mL (Roche), 1.6 mM PMSF, 1 mM DTT, and 0.1 mM ATP. The brain lysate was gently stirred for 30 min at 4°C until completely thawed and was then spun in a JLA 8.1 fixed angle rotor (Beckman Coulter) at 8,000 r.p.m. for 45 min at 20 °C. The supernatant was collected and further clarified using a Type 45 T.i. rotor (Beckman Coulter) at 45,000 r.p.m. for 50 min at 4°C. The supernatant was then filtered with a glass-fiber filter (Sartorius), followed by a 0.45 μm filter (Milex Millipore). The sample was loaded into a SP-Sepharose column (Cytiva) pre-equilibrated with SP buffer  using an Akta FPLC (Cytiva). The column was washed with 4 column volumes of SP buffer in the presence of 3 mM KCl followed by elution using a linear gradient up to 250 mM KCl. Fractions collected from the first major peak were pooled, filtered using a 0.22 μm filter (Milex Millipore), then loaded onto a MonoQ 16/10 column (Cytiva) pre-equilibrated with MonoQ buffer . The column was washed with 10 column volumes of MonoQ buffer followed by elution using a linear gradient up to 150 mM KC1 in 1 column volume, a second linear gradient up to 350 mM KCl in 10 column volumes, and a third linear gradient up to 1 M KCl in 1 column volume. The peak dynactin fractions located at approximately 38 mS cm−1 were then pooled and concentrated to a volume of about 3 mL and loaded onto a G4000SW 21.5/600 column (Tosoh Bioscience) equilibrated with GF150 buffer  for gel-filtration. The dynactin peak collected, pooled, and concentrated to approximately 2 mg mL−1 using an 0.5 mL 100 kDa MWCO filter unit (Millipore). 2 μL aliquots were then flash frozen in liquid nitrogen and stored at −80 °C.Native dynactin was purified from pig brains (Yosemite Foods)280 using Nanodrop 1000 (Thermofisher).The phi mutant of dynein, KIF5B, MAP7, and truncated TRAK1, and TRAK2 constructs were purified from baculovirus-infected SF9 cells (UC Berkeley Cell Culture Facility). Briefly, cell pellets were resuspended in a lysis buffer (see below) supplemented with cOmplete Protease Inhibitor Cocktail (Roche) and lysed using a dounce homogenizer (Wheaton) (20 strokes with loose plunger followed by 20 strokes tight plunger). The cell lysate was clarified by centrifugation at 186,000 g for 45 min, incubated with IgG Sepharose (Cytiva) for 2 hr at 4 °C, applied to a gravity flow column, and washed extensively with a TEV wash buffer (see below). The protein-bead complexes were then treated with TEV protease  at 12 °C overnight. The mixture was then centrifuged at 4000 g for 5 min and the supernatant was concentrated using an Amicon Ultra 0.5 mL spin column (EMD Millipore). Protein concentration was determined by measuring the OD, IgG beads were washed with a kinesin TEV wash buffer , and the protein was concentrated using 100 K MWCO spin filter. MAP7 purification was performed using a MAP7 lysis buffer  and MAP7 TEV wash buffer . MAP7 was concentrated using a 50 K MWCO spin filter. Truncated TRAK constructs were purified in the presence of high salt, glutamic acid, and arginine to improve solubility , and protein was concentrated using a 50 K MWCO spin column. Full-length TRAK1 and TRAK2 were purified from HEK 293 F GNTI-/- cells (UC Berkeley Cell Culture Facility) using polyethyleneimine  transfection of the pcDNA and the TRAK purification procedure described above.Different buffer conditions were used for each protein preparation. Cells expressing dynein were lysed in a dynein lysis buffer , IgG beads (Cytiva) were washed with a dynein TEV wash buffer  and the protein was concentrated using a 100 K molecular weight cut-off (MWCO) spin filter (EMD Millipore). For kinesin purification, the cells were lysed in a kinesin lysis buffer 1–592 (Miro11–592-SNAP-psc-StrepII) was purified using Miro lysis buffer  supplemented with 1x protease inhibitor cocktail (Roche) and lysed using a dounce homogenizer. The lysate was clarified by centrifugation at 65,000 g for 45 min, incubated with Streptactin Sepharose beads (IBA) for 2 hr at 4 °C, applied to a gravity flow column, and washed extensively with Miro wash buffer . The protein was then eluted from beads with 3 mM desthiobiotin and concentrated using a 50 K MWCO spin column (EMD Millipore).Miro11–473 was purified from BL21(DE3) E. coli cells (UC Berkeley QB3 MacroLab). Briefly, cell pellets were resuspended in SNPH lysis buffer  supplemented with 1x protease inhibitor cocktail (Roche) and lysed using a tip sonicator (Branson) for 2 min. The lysate was clarified by centrifugation at 65,000 g for 45 min, incubated with IgG Sepharose beads for 2 h at 4 °C, applied to a gravity flow column, and washed extensively with SNPH wash buffer . The protein-bead complexes were then treated with TEV protease at 4 oC overnight. The mixture was then centrifuged at 4000 g for 5 min and the supernatant was concentrated using a 50 K MWCO spin column (EMD Millipore).SNPH4, 1 mM EGTA, pH 7.0). Samples were then diluted 1:2 in MB supplemented with 100 mM NaCl, 1 mg ml−1 BSA, and 1 mM DDT and incubated on ice for 2 h to facilitate complex formation. 10% of samples were collected for input lanes and then washed 3x with the MB buffer including supplements. Samples were then resuspended into LDS sample buffer (Invitrogen), boiled for 10 min at 95 oC, and run on an SDS-PAGE gel. Imaging was performed on a GE Typhoon FLA fluorescence imager.For each sample, 10 µg of each protein construct was pre-mixed on ice for 5 min and then added to 15 µL of GFP-Trap beads (Chromotek) that were pre-washed with the MB buffer  followed by incubation for 30 min at room temperature in the presence of 1 μM Sfp phosphopantetheinyl transferase (Addgene #75015)to catalyze protein labeling.Proteins were labeled with fluorescent probes before they were eluted from the affinity columns. For SNAP labeling, IgG bead slurry (Cytiva) was concentrated to 5 mL, followed by the addition of 5 nmol of either BG-LD555 or BG-LD655 dye (Lumidyne), followed by incubation for 1 h at 4 −1 BSA-biotin (Sigma) was introduced into the flow chamber, which was then washed with MB buffer supplemented with 1 mM DTT, 10 μM taxol, 1.25 mg ml−1 casein (Sigma) and 0.5% pluronic (MBCT). MBCT was additionally supplemented with 0.2% methylcellulose and 50 nM K-Acetate for DDKT motility assays and dynein motility assays with TRAK11–532. The chamber was then incubated with 20 µl 1 mg ml−1 streptavidin (NEB) in MBCT and washed with 40 µl MBCT. For imaging dynein motility, fluorescently-labeled dynein, dynactin, and a cargo adaptor (TRAK1 or TRAK2) were mixed at a 1:5:20 molar ratio in MB for TRAK11–400 and TRAK21–400 and a 1:3:10 molar ratio for TRAK11–532, respectively. Miro11–592, dynein, dynactin, and TRAK11–532 were mixed at a 1:3:10:30 molar ratio, respectively, in the presence of 1 μM Lis1. For imaging kinesin motility, kinesin, a cargo adaptor (TRAK1 or TRAK2), MAP7, and Miro11–592 were mixed at a 1:3:3:3 molar ratio, respectively, in MB buffer. For imaging both dynein and kinesin simultaneously, dynein, dynactin, kinesin, and a cargo adaptor were mixed at a 1:3:1:1 (TRAK1) or 1:5:1:1 (TRAK2) ratios, respectively, in MB buffer with the stated concentration of MAP7. The mixtures were incubated on ice for 10 min and diluted 30-fold in MBCT. Finally, the mixture was diluted 10-fold in the stepping buffer (MBCT supplemented with 0.1 mg ml−1 glucose oxidase (Sigma), 0.02 mg ml−1 catalase (Sigma), 0.8% D-glucose, and 1 mM Mg·ATP) and introduced into the chamber. Motility was recorded for 5 min. For assays including Miro11–592, 0.1 mg ml−1 biotin-BSA was also included in the stepping buffer for surface passivation.To immobilize biotinylated MTs to the coverslip, 1 mg ml−1, Covance) was flown into an assay chamber and incubated for 3 min. The chamber was washed with 30 μl MB supplemented with 1 mM DTT, 10 μM taxol, and 1.25 mg ml−1 casein. For kinesin-driven MT gliding, 10 μl of 2.5 nM GFP-tagged kinesin was subsequently added to the chamber. In the case of DDT-driven MT gliding, 10 nM dynein, 10 nm dynactin, and 10 nM GFP-TRAK21–400 were incubated in MB on ice for 10 min in the presence of 1 µM Lis1, and 10 μl of this mixture were added to the chamber. After 2 min incubation, the unbound motor was removed by washing the chamber with 30 μl MB. For experiments with SNPH, 10 μl of SNPH1–473-sfGFP was added to the chamber at the indicated concentration for 2 min followed by a 30 μl MB wash. Then, 10 μl of 200 nM Cy5-labeled MTs were flown to the chamber and allowed to bind the kinesin- or DDT-decorated surface for 4 min. The chamber was then washed with 60 μl MB. Lastly, 10 μl of imaging buffer  was flown into the chamber to initiate gliding motility.Rabbit monoclonal anti-GFP antibody  through a Nikon TIRF Illuminator, and their fluorescent emissions were filtered through a notch dichroic filter and 525/40, 585/40, and 655/40 bandpass emission filters (Semrock), respectively. Multicolor fluorescence imaging was performed using the time-sharing mode in MicroManager. Videos were recorded at 2-4 Hz.Fluorescence imaging experiments were performed using a custom-built multicolor TIRF setup equipped with a Ti-Eclipse inverted microscope body, a 100X magnification 1.49 N.A. apochromat oil-immersion objective (Nikon), a perfect focusing system, and an electron-multiplied charge-coupled device camera  with an effective pixel size of 160 nm after magnification. Alexa488/GFP, LD555, and LD655 probes were excited with fiber-coupled 0.05 kW cm54. Videos were analyzed in ImageJ. Kymographs were generated by plotting segmented lines along the MTs using a custom-written ImageJ macro. The processive movement was defined and analyzed as described previously27. Complexes that exhibited diffusive movement, ran for less than 250 nm, and paused for more than 1 s were excluded from velocity analysis. For two-color imaging, the fluorescence channels were overlaid in ImageJ to generate a composite image. Colocalization events were manually scored in kymographs. For two-color imaging of a motor and a cargo adaptor (TRAK1 or TRAK2), processive motility events observed in the cargo adaptor channel that did not colocalize with a motor were still included in the velocity analysis.Coiled-coil prediction scores of human TRAK1 and TRAK2 were calculated from the NPS@ server using the algorithm of Lupas et al.11–400 and DDT21–400 complexes were assembled with 1 µl of 0.84 mg ml−1 dynein, 1 µl of 1.7 mg ml−1 dynactin, and 1 µL of 0.22 mg ml−1 TRAK11–400 or 0.1 mg ml−1 TRAK21–400 in MB for 5 min at 4°C. The protein mixture was then added to 700 nm diameter polystyrene beads (Invitrogen) coated with a polyclonal GFP antibody (Covance) and incubated for 10 min. Similarly, KT11–400 complexes were assembled with 1 µl of 0.05 mg ml−1 biotinylated kinesin-ybbR and 1 µl of 0.1 mg ml−1 TRAK11–400 before being added to 700 nm diameter streptavidin-coated beads (Spherotech). Flow chambers were first decorated with Cy5-labeled sea urchin axonemes in MB. The motor-bead mixture was introduced to the chamber in the imaging buffer. To ensure that more than ~95% of beads were driven by single motors, the protein mixture was diluted before incubating with beads such that a maximum of 30% of beads exhibited activity when brought into contact with an axoneme.DDT55. Briefly, motor-coated beads were trapped with a 2 W 1,064-nm laser beam (Coherent) focused on the image plane using a 100X magnification 1.49 N.A. apochromat oil-immersion objective (Nikon). Cy5-labeled sea urchin axonemes were excited with a 633-nm HeNe laser (JDSU Uniphase), imaged using a monochrome camera (The Imaging Source), and moved to the center of the field of view using a locking XY stage . The trapped bead was lowered to the surface of the axonemes using a piezo flexure objective scanner . Bead position relative to the center of the trap was monitored by imaging the back-focal plane of a 1.4 N.A. oil-immersion condenser (Nikon) on a position-sensitive detector (First Sensor). Beam steering was controlled with a pair of perpendicular acousto-optical deflectors (AA Opto-Electronic). For calibrating the detector response, a trapped bead was rapidly raster-scanned by the acousto-optical deflector and trap stiffness was derived from the Lorentzian fit to the power spectrum of the trapped bead. The spring constant was set to ~0.04 pN nm−1 for DDT11–400 and DDT21–400, and ~0.08 pN nm−1 for KT11–400 experiments.Optical trapping experiments were performed on a custom-built optical trap microscope set-up controlled using Labview 2017 softwareCustom MATLAB software was used to extract stall forces and stall times from raw traces. First, raw traces were downsampled from 5,000 Hz to 250 Hz. Stall events were defined as a stationary period of a motor at forces above 2.5 pN lasting a minimum of 100 ms, followed by snapping back of the bead to the trap center. The stall force was defined as the mean force in the last 20% of the stall event. The stall time was defined as the interval the bead spent at a force of at least 80% of the stall force. All stall events were plotted and manually reviewed to confirm the accuracy of the reported values.n) and statistical analysis methods are clearly stated in the figure legends. Representative data are shown from independently repeated experiments.At least two independent repetitions were performed to obtain any given result. The number of replicates (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Reporting Summary


Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the “pAItient”  project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center.The first part of the pAItient project aims to explore stakeholders’ requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data.We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants’ answers and distributed among the stakeholders’ organizations. In addition, patients and physicians were interviewed.The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium.The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications.RR2-10.2196/42208 Considering the current and future challenges in health care, such as lack of health care workers and global health, artificial intelligence (AI) is regarded as one possible part of a solution to address these problems -4. WhileThe topic of AI in health care has recently received a significant amount of attention in research, policy making, and in the general population -8. HowevAnother uncertainty pertains to the perspective of patients regarding the use of their medical data for AI development. Aitken et al  conducteIt is plausible to assume that the preferences and opinions derived from previous research could vary between different cultural contexts and between the general population and current patients. To the authors’ knowledge, there are no previously reported findings on patient perceptions of data usage for AI research in Germany.pAItient project , which has the objective to establish an AI innovation environment as a proof-of-concept extension of the already existing Medical Data Integration Center . Personally, I would much rather like to see a result for the patient or in patient care.PG1_1, Transcript position 68In the anonymous survey, participants from groups 1 and 3 rated patents as one of their second lowest or least important goal, respectively. Interview participants from private companies emphasized achieving business goals .Survey participants were asked to rate their approval to the goals derived from the interviews on a slider ranging from 0% to 100%. All statements concerning intellectual property (IP) were assigned to this theme. IP was seen as a risk that has to be managed to be able to access data.I think at the moment – this topic [IP] is not as urgent, because lack of data is such a big problem and many AI-developers will take this risk, many risks, to access data.PG1_2, TP28In general, interviewees had the opinion that the institution that designed the algorithm is entitled to IP. They were in favor of regulating these questions via contracts with the institutions providing the data. Considering contracts, licensing options were viewed unfavorably because they provide only temporary access. While IP was a topic of lower importance, differing IP regulations were seen as a measure to compare data-providing institutions with each other.Another aspect that was discussed in the interviews concerned data processing and in what kind of IT infrastructure the data derived from the hospital should be analyzed, trained, and validated. Researchers from group 1 highly favored having the data easily accessible within their own infrastructure. Yet, they were also accepting of the idea to access data through remote access options. Necessary antecedents for this approach were a good UI and a stable connection. Advantages of this option were the ability to view the data within their original system architecture and that no exchange of sensitive patient data would be necessary.If that works well, the UI, the stability – and you can also get enough information about the data, so you are able to decide how to build your model. In that case, I think that makes sense and it would also make a lot of things easier, because you don’t have to exchange data all the time.PG1_3, TP30A possible disadvantage was the risk that a hospital’s technical infrastructure could lack the computing power to train algorithms. To counter this risk, 1 participant suggested to use a smaller subset of the data outside the hospital system and to conduct the validation within the hospital system.Survey participants were asked to indicate if they use or do not use specific hardware and software for the development of AI tools. The results of this question are presented in This question asked interview participants to indicate and explain which data or type of data they typically use for their development processes. They stated that they would ideally work with annotated data and that the preceding annotation workflow should be made transparent to them. The availability of data with high-quality annotation reduces the amount of data needed. Concerning specific use cases, they would also like to access data other than patient data/medical records, for example, data from surgical instruments  and data from external sources .Survey participants were asked to check a box if they typically worked with these kinds of data. A further aspect that was discussed with interview participants was the cooperation with academic hospitals and previously experienced or anticipated advantages and disadvantages of this cooperation. Possible advantages that were mentioned included the availability of high amounts of data, special data , the possibility to test and validate algorithms within a protected environment, and the hospital’s willingness to invest additional effort in research projects. Another advantage was the proximity to a “real environment” in a hospital setting:So [from working with an academic hospital] you don’t only get artificial data or only specific data from special cases but the kind of cases that typically reoccur in a hospital. So, they are from a “real environment”. And the closer you can get to the actual environment, the easier you can develop an application that is able to deliver good results.PG1_3, TP24Survey participants were asked to rate their agreement with the mentioned advantage on a 5-point Likert scale . Besides advantages, potential disadvantages of these cooperations were discussed. Interview participants described bad experiences with extensive bureaucracy, which made getting access to data difficult. Administrative contact persons in the hospitals that they had encountered previously were remembered as anxious to violate data privacy regulations. This observation, in combination with lacking technical knowledge to understand the research proposals, has previously led to uncertainties, delays, and to a “play it safe” approach regarding research ideas.And the technologies are getting more and more complex. And this uncertainty, which is understandable, can result in a tendency to “play it safe”. And this can, of course, lead to problems or exhausting processes, at least from a researchers’ perspective, who are always like “this is great, let’s get started immediately.PG1_2, Pos. 32Sometimes, data sets they had received were not self-evident and consultations with hospital physicians were necessary to work with the data. In addition, data sets they had been offered were hard to use and not in the ideal format . Interviewees also mentioned that the necessity to work within a research study setting introduced further difficulties, such as having to get approval from the ethics committees. Another risk that has to be managed is “overfitting,” which could result from a too strong reliance on data from 1 source.Survey participants were asked to rate their agreement with the mentioned disadvantage on a 5-point Likert scale . This code was assigned to all statements concerning potential areas where academic hospitals could support the participants’ institutions. Interview participants suggested the installation of a platform enabling a quick overview over which algorithms had already been developed or tested at the hospital in question, which groups are working on projects related to AI, and which data are available and could be used for the development of AI tools. The interviewees argued that a platform like this could improve cooperation, innovation, and creativity.To establish an environment to look at data, meta data – to get a good insight how the data are available, what you can do with them. […] And you should also be able to see what has already been done, for example, what are the parameters for a deep leaning model somebody has set up. […] I know this is not easy. But that is how I think a platform like this should work, it should enable a lot of exchange between researchers. […] This is how you can find new partners for cooperation. You can easily see which group is developing new expertise in which area. And so you can find new people easier.PG1_3, TP36Interviewees also suggested a closer cooperation with clinicians, for example, in finding common goals. Here, clinicians could give more insight into which algorithms or software solutions could make their work easier. Clinicians could also support in the evaluation of newly developed tools to determine whether these are usable and beneficial for clinical practice. At the same time, interviewees would also like to be able to explore data independently. This independent exploration should be supported by platforms with a good UI and characteristics about the provided data should be communicated clearly . In this context, participants suggested the introduction of new tools into the physicians’ workflow, which will allow for the parallel annotation of collected data.Survey participants were asked about their perceived usefulness on a 5-point Likert scale . A total of 6 patients were recruited successfully. One patient withdrew their consent after the interview. Interviewees were asked to fill in a sociodemographic questionnaire. Following the thematic analysis  approachThis code was assigned to all statements concerning AI technologies in general, such as previous knowledge, hopes, and fears. Patients in this sample had high hopes of AI possibly helping in the treatment of their own disease in the future and thus were optimistic about their participation in the study and its aims.I am happy to see that research in this area is happening here. It is tangible. [...] That is also one of the reasons why I chose to participate in this study, I am curious.PG5_2, TP56Although none of the participants in this study said that they themselves had fears or negative perceptions surrounding AI, they recognized the prevalence of these fears within the society:A lot of people have this negative image in their head. Robots are taking over, mankind can’t do anything anymore and is reigned by AI, by a machine. And this image creates fear. That is understandable. […] But I am very positive towards robots and AI.PG5_1, TP30The interviewees also discussed that these fears and a lack of understanding of AI could lead to misunderstandings and low willingness among other patients to agree to the use of their data.The patients in this sample were also optimistic about the potential use of their medical data for the development of AI tools and mentioned several potential positive effects. Again, they saw a potential to support research and treatment in their disease.I think my treatment data could maybe help patients, who will be affected by the same disease later. So it could help in the actual treatment of this disease.PG5_5, TP8I have stage IV breast cancer and would share as much data as necessary. I would have no problem at all to share personal data or health data and so on. I would see that as an opportunity.PG5_4, TP12Concerning the kind of data that can be shared, they only noted that personal data such as name, phone number, and email address should not be shared. They emphasized that medical data should be shared in a form which does not allow for reidentification. Participants further demanded to be informed about what will happen with the data they shared, why their data will be necessary, and what other organizations will be involved. In general, they supported an option to limit the kind of data that can be shared. However, they also argued that many other patients could lack the necessary knowledge and information to judge these issues. In this context, they also worried that physicians or other contact persons might not have enough time to explain the data-sharing concepts to all their patients, especially patients with lesser previous knowledge.The lack of time for necessary explanations was also mentioned as a reason why the patients in our sample would potentially refuse to share their data. Other reasons included, for example, if there is a risk of reidentification of their data, if the benefit for medical research is not stated clearly, and if the reputation or trustworthiness of the institution they are being treated at is bad.Generally, participants highly valued and trusted the existing data privacy regulations in public institutions. Yet, patients were sceptic toward the involvement of too many different organizations. This was explained by a perceived higher risk of data leaks or misuse when too many players are involved. Skepticism was especially notable toward private companies. Here, participants clearly differentiated between private companies and publicly funded institutions:Public institutions are good. But as soon as private companies are involved, I would like to have transparency and would like to know which companies.PG5_4, TP47On the other side, interviewees stated that having a bigger group with several different organizations also means that research could be done faster and better due to more researchers working on the same questions.Concerning the consent process for data sharing, patients preferred to have a conversation with their physician in which the physician would take time to inform them about the data-sharing process and answer potential questions. In general, the person conducting the consent process should be trustworthy and well informed. Brief, written information should be handed out beforehand, so that patients are able to prepare questions. Participants demanded to be informed about which companies are involved, a very broad overview over what will be done with their data, and how their data will be protected.There has to be a relationship of trust with this person. It is not really relevant whether that is a senior or junior physician or research associate, it just has to be made clear what will happen. And another thing would be important for me – will the data only go to academic hospitals or will they also be transferred to pharmaceutical companies? And under which conditions?PG5_3, TP48Over the course of the individual interviews, the requirements for consent to data sharing for AI development emerged. Participants stated that they needed to be able to retract their consent. The institution they are sharing their data with has to be trustworthy and their data should not be reidentifiable. If private companies are involved, a medical benefit for patients should be the objective. Finally, patients demanded to receive transparent and comprehensive information beforehand.The General Data Protection Regulation (GDPR) was introduced in 2018 by the European Parliament and Council of the European Union . It inclpAItient project, the results from this study were combined with the findings from participant group 6 (physicians), which were reported by Kamradt et al [To merge the findings from all the studied participant groups within the dt et al . In addiThis model was communicated to all project members. The requirements were operationalized and items relevant to the development of the IT architecture were implemented in the IT concept. This guarantees consideration of these requirements in the long term of the AI innovation environment.pAItient project consortium.The aim of this study was to collect the requirements for the development of AI-based algorithms based on routine medical data within a hospital-based generic infrastructure. Views from different stakeholder perspectives were included in this study, such as patients, physicians, AI researchers, and industry employees. Legal requirements were deduced from the literature. The identified requirements covered a wide range and were inherently conflicting sometimes, even within the stakeholder groups. However, a requirement model was developed, which depicts the identified requirements in 4 different layers. In the center, the most relevant patient requirements for data use were listed . The second layer represents the physicians’ requirements for participating in AI research and development projects  . The thiComparable projects to build infrastructures and networks enabling AI development are ongoing, such as the CHAIMELEON project . HoweverConcerning the findings from interviews with patients, previously reported requirements could be replicated in our sample. McCradden et al  conducteThe importance of respecting patient requirements for data use in AI development is evident, as many studies have shown that patients’ wishes and expectations for the use of their data in health research can differ from researchers’ wishes . Tosoni A limitation for this study is the relatively small sample size. Concerning groups 1-4, this could be explained by a lack of time of potential participants due to a high number of concurrent projects and research activities. To increase the number of potential participants, the survey was translated into English. This led to an improvement in response numbers. The survey results were very homogenous, with mostly high rates of approval. This could imply that the interview participants represented their institutions well, indicating that the results from these groups are plausible despite the small sample size. Nonetheless, the possibility of missing potentially relevant aspects cannot be disregarded.Recruiting patients for the interviews was challenging as well. This could be explained by the complexity of the topics of AI and data use, resulting in a low interest in participation in research . This hapAItient project, another qualitative study is planned at the end of the project to evaluate the AI innovation environment and to revisit the requirements presented in this study.The study presented in this paper led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A 4-layer model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. Results will play a major role in the design and implementation of our infrastructure and processes of the AI innovation environment. In the context of the


Single-cell transcriptome analysis of zebrafish cells clarifies the signalling pathways controlling skin formation and reveals that some cells produce proteins required for human teeth to acquire their enamel. Related research article Aman AJ, Saunders LM, Carr AA, Srivatsan SR, Eberhard CD, Carrington B, Watkins-Chow D, Pavan WJ, Trapnell C, Parichy DM. 2023. Transcriptomic profiling of tissue environments critical for post-embryonic patterning and morphogenesis of zebrafish skin. eLife12:RP86670. doi: 10.7554/eLife.86670.The largest organ in the vertebrate body, the skin, performs a wide range of roles such as protecting against infection, sensing the environment, and supporting essential appendages such as hair, feathers and scales. It is also beautifully complex.In its postembryonic form, vertebrate skin is formed of three layers – the epidermis (the outermost layer), the dermis and the hypodermis – that contain a range of different cell types, each dedicated to a specific function. In zebrafish, for example, some cells create the proteins required for scales to harden and become calcified, while others produce the pigments that give the species its delicate stripe pattern. Despite extensive studies over the past few decades, researchers still do not fully understand how this complexity arises during development. Now, in eLife, David Parichy and colleagues – including Andrew Aman and Lauren Saunders as joint first authors – report that they have classified all the major cell types in zebrafish skin, identified a cell type which was previously unknown, and dissected some of the signalling networks that are essential for development .The researchers – who are based at the University of Virginia, the University of Washington and the National Human Genome Research Institute – started by using single-cell transcriptomic analysis to study 35,114 post-embryonic zebrafish skin cells. This approach allowed Aman et al. to establish the ‘RNA profile’ of each individual cell, showing which genes it expresses, and at what level, at a given time.One of the most interesting findings to emerge from this work was the identification of a group of epidermal cells which expressed genes coding for proteins that are necessary for the formation of enamel . As humaTo better understand the molecular mechanisms underpinning skin development, Aman et al. applied their approach to cells from various zebrafish mutants . In animpdgfaa. Further in vivo work showed that over-expressing this gene in fish with low levels of thyroid hormone partially re-established a normal stratification of the dermis, but did not alter how scales were created. Together, these findings should open new opportunities for understanding and treating human skin diseases.Finally, Aman et al. examined the role of the thyroid hormone on skin development, as this chemical messenger has been implicated in a range of human skin conditions. To do so, they examined the RNA profiles of skin cells from zebrafish in which the thyroid gland had been removed . This anThis work illustrates how single-cell transcriptomic profiling can detect rare cell types, infer cell fate trajectories, and identify relevant signalling networks. On its own, however, this method may fall short of capturing the exquisite details of skin development, such as how differentiated skin cells influence the behavior of neighbouring basal stem cells, the way that appendages instruct the growth of nerve projections and blood vessels, or the fact that tension can trigger skin cells to divide without replicating their DNA . Only stZebrafish skin may seem less sophisticated than ours at first glance, but Aman et al. have undoubtedly demonstrated that there is much to discover beneath its surface. Developmental biologists can glean valuable insights from looking into it more closely. Given the evolutionary connection between teeth and scales, and now the shared presence of ameloblast-like cells in zebrafish and humans, it may even become possible to unravel why scales, but not human teeth, can regrow throughout life. While it is probably a wild guess, it is fascinating to imagine that one day we may be able to regenerate human teeth thanks to findings made in a toothless little fish.