dataesr/scientific-paragraphs-categorization huggingface dataset:
This showed that the stimulation cells' labels from their neighbors in starvation data were assigned at the same accuracy as the test starvation data, meaning that clusters from the starvation data are applicable to the stimulation dataset and capture the main features of the stimulation dataset to the same extent (fig. S6A ). We also examined batch effects in the stimulation experiment. As with the starvation experiment, the L 1 metric was used to visualize the magnitude of batch effect within the multiplexed experiments compared to between experiments (fig. S8 ). We used the merged representation (used for the KNN assignment) between both experiments to find the average pairwise distances between cell types of control condition individuals within the starvation experiment, within the stimulation experiment, and across both experiments.
RNA-seq analysis and clustering with MARIMBA annotation To make our dataset more easily searchable with the MARIMBA v. 1 (created on 05/30/2016) transcriptome annotation  (21) , we generated single-cell gene count matrices with respect to transcript sequences distributed via the MARIMBA website (http://dx. doi. org/10. 22002/ D1. 1830). The gene count matrices were generated using the same kallisto-bustools workflow. We compared the application of the previous clustering/cell type assignment (including the overlap in differentially expressed genes delineating these clusters) to validate the quantification derived from the Trinity/Cuffcompare assembled transcriptome (fig. S13 ). We also produced a notebook for visualization of gene expression in this dataset to facilitate its use in future studies.
Pseudo-time analysis We selected cells from cell types of interest (i-cells, nematoblasts, nematocytes, and neural cells) and used diffusion maps  (73)  to create a reduced dimension representation of cells, along with Scanpy's dpt function that uses geodesic distance along the graph of cells (in the determined "diffusion component" space) to estimate pseudotime. We then computed a PAGA-based embedding to visualize the cells in the context of the different trajectories with a ForceAtlas2 layout. To determine which genes constituted the important features in a given pseudo-time trajectory, we implemented a method based on the random forest method used in the dynverse R package for extracting "important" genes  (74). A random forest regression model implemented with sklearn's random:forest_regressor was used to identify genes that were good predictors of the generated pseudo-time values (grouped into quantiles).
This was run for each of the two inferred trajectories separately (stem cells to nematocytes, and stem cells to neurons). The training set consisted of 80% of the genes' expression data, within which 80% was used for optimizing the model, and 20% were used to evaluate the mean squared error (MSE). Both trajectory models had an R 2 (coefficient of determination) of 0. 85 or greater. The remaining 20% of data from the full dataset were used to calculate the gene-wise permutation importance scores, providing a ranking of each feature (gene) in terms of its contribution to the model's predictive capabilities using sklearn's permutation_importance. In the ranking, positive scores indicate importance, with the importance i j for gene j being the difference between the original score s (MSE) calculated from the original model, and the average score across K = 5 random permutations of the feature columns in the validation dataset i j = s -1 ─ K ∑ k=1 K s k,j Genes with nonzero (positive) scores in the permutation test were retained and ranked (table  S5 ).
Acknowledgments: We thank  X. Da  and  X. Wang  for technical assistance,  T. Momose  for assistance with the single-cell experimentation, the  Caltech Single-Cell Profiling and Engineering Center  for the use of their single-cell and sequencing tools, and the  Caltech Bioinformatics Resource Center  for transcriptome assembly and annotation analysis. We additionally thank the  Caltech Center for Evolutionary Science  for the bioinformatics resources to create a local  UCSC Genome Browser. We thank  A. S. Booeshaghi  for help with kallisto, bustools, and the kITE demultiplexing of the ClickTag reads and for rescuing the stimulation experiment sequencing data. We thank  J. Malamy  for helping to establish Clytia work at  Caltech. We thank  S. Peron  for initial characterization of some of the cell type marker genes,  P.
Lapébie  for identification of novel neuropeptide sequences,  M. Jager  for valuable advice on the in situ protocol, and  J. R. Mateu  for providing pp11 probe. Funding: J. G. , M. H. , and L. P. were supported in part by a seed grant from the  Chen Institute at the California Institute of Technology. T. C. , J. G. , and L. P. were supported in part by  NIH   U19MH114830  and  NIH RF1AG062324A. We thank the  Marine Resources Centre (CRBM and PIV imaging platform) of Institut de la Mer de Villefranche (IMEV) , supported by  EMBRC-France. The French state funds of EMBRC-France are managed by the  ANR  within the investments of the  Future program. L. L. was supported by the  Agence Nationale de la Recherche  ( ANR-19-CE13-0003 ). A. F. , R. R. C. , and E. H. were supported by the  H2020/Marie Skłodowska-Curie ITN "EvoCell" Grant  agreement no.
Except for structured illumination microscopic (SIM) analysis where Vectashield medium was used, all staining were mounted in Mowiol. Live imaging. Live cell spreading and migratory assays were performed with the Biostation (Nikon, Tokyo, Japan) using the ×20 objective. Time-lapse images were taken every 10 min for 2-4 h. Cell were treated with mitomycin C (10 μg/ml; Sigma-Aldrich) for 1 h to prevent division, before seeding on collagen-coated glass bottom fluorodishes (#FD35-100, World Precision Instruments, Sarasota, FL, USA). Cell track measurements and graphs were obtained using MATLAB (Mathworks, Natick, MA, USA). Actin dynamics experiments were performed using an inverted DMI8 Leica microscope equipped with a CSU-W1 spinning disk head (Yokogawa-Andor), using a ×100 1. 4 NA oil objective and Metamorph (7.
6, ×63 objective, and a camera EM CCD Andor SIM. During z-stack acquisition, five rotations were applied. Deconvoluted structured illumination images were generated by the Zen software, and images were merged in ImageJ. Atomic force microscopy. Cells were cultured in DMEM, 20% FBS, 1× penicillin-streptomycin, and 2 μg/ml puromycin. Plastic petri dishes (TPP, Switzerland) were incubated in 100 μg/ml rat tail collagen I (Gibco A10483-01) in 0. 1% acetic acid at 4 °C overnight on a 60 rpm shaker. Cells were seeded at low density and allowed to adhere at least 5 h. AFM nanoindentation experiments were performed with a Nanowizard 4 (JPK Instruments, Germany) in Quantitative Ima-ging™ mode. The imaging buffer was Leibovitz L-15 medium supplemented with 20% FBS and 1× penicillin-streptomycin and experiments were performed at 37 °C with a petri dish heater.
PFQNM-LC-A-CAL cantilevers (Bruker, USA) were used; the tip was parabolic (Hertz model) at the apex with r = 35 nm as determined by scanning electron microscopy (see Supplementary Fig. 3a, b ). Electron microscopy was performed using a JEOL JSM-6010LV microscope. The probe spring constant was provided by the manufacturer (k = 0. 068 N/m), and the optical lever sensitivity was determined by the thermal tuning method. Force-indentation curves were collected with 100 μm/s probe velocity, 400 pN trigger force, and variable indentation-retraction distance (scanning frequency) over a (60 μm) 2 area with 128 × 128-pixel resolution (each cell scan lasted ~10 min, the fast axis was horizontal in images shown). Data were analyzed using a custom-built MATLAB program adapted from previous work  134, 135.
This MATLAB-based custom program for AFM analyses  136  is available at https://github. com/bryantdoss/matlabafm-indentation (https://doi. org/10. 5281/zenodo. 4543281). Force-indentation curves were fit by inverting the power-law of the force-indentation curve and then fitting this to a linear slope and x-intercept to determine the apparent elastic modulus and contact point, respectively  134  ; indentation depths up to 800 nm were analyzed and the Poisson's ratio is assumed to be 0. 5. All force-indentation curves were first corrected for virtual deflection and initially fit using the Hertz model for the contact point and an initial modulus without any correction for the rigid substrate. Very stiff regions (>65 kPa) were assumed to be the plastic substrate, and the height of all locations was determined by linear flattening of the contact points on plastic regions.
Cells were plated on the substrate and imaged for 22-24 h, at a 6-min interval in the Biostation using the ×20 objective. Images were aligned to compensate for experimental drift before analysis of the beads displacement was performed, using Particle Imaging Velocimetry script in MATLAB. From the displacement data, Fourier transform traction cytometry plugin in ImageJ (available at https://sites. google. com/site/qingzongtseng/tfm) was used to estimate the traction forces exerted on the substrate. The total traction force exerted by cells was calculated by summing the magnitudes of the traction vectors under and near the cell of interest and multiplying by the area covered by those vectors. FRET analyses. The FRET probe Raichu-1502 (also named Raichu-RBD  81  ) was transfected into Caco2 cells 48 h before the experiment.
Spectral imaging was performed on a confocal LSM780 microscope (Zeiss, Zen software) with ×63/ 1. 4NA plan apochromat oil-immersion objective. CFP was excited by the 458-nm laser line of an Argon laser, and emission was sampled at a spectral resolution of 9nm within a 444-570-nm range. ImageJ was used to process images for analyses. FRET ratio was calculated as the ratio between the YFP and CFP signal. Statistical analysis. All statistical analyses were performed using Prism (GraphPad Software, San Diego, CA, USA, version 7. 0). Unless otherwise stated, experiments were replicated three times independently and comparison between samples were done without Gaussian distribution assumption of the data, meaning comparisons were carried out using Mann-Whitney test for two conditions comparison, or Kruskal-Wallis test and Dunn's multiple comparison tests for three and more conditions.
p Values met the following criteria *p < 0. 05, **p < 0. 01, and ***p < 0. 001. Triple colocalization was analyzed from confocal stacks using a MATLABbased custom program. Briefly, fluorescence was first segmented in each channel using local thresholding (Phansalkar method  137  and a local two-dimensional median filter with user-defined 16 neighborhood size was applied to remove noise. Colocalization was then measured using the Manders split coefficients M1 and M2  138   , where S1 i,coloc = S1 i if S1 i > 0 AND S2 i > 0 and S2 j,coloc = S2 j if S2 j > 0 AND S1 j > 0. The MATLAB-based custom program for triple colocalization analysis and the standalone user interface  139  are available at https://github. com/simondebeco/ Colocalization-Analyzer (https://doi. org/10. 5281/zenodo. 4501944).
Acknowledgements We thank  Alpha Yap ,  Arnaud Echard , and  René-Marc Mège  for helpful discussions. We thank  Sarah Benlamara ,  Arthur Radoux , and  Dylan Gourmelin  for technical help. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of  France-BioImaging  ( ANR-10-INBS-04 ) infrastructure. This work was supported by grants from the  Groupama Foundation -Research Prize for Rare Diseases 2017  (to D. D. ), the  Fondation pour la Recherche Médicale  (FRM, # FDT201805005185 ) (to C. G. ), the  Association Prolific (prix Graine de Chercheur 2017)  (to C. G. ), the  LabEx Who Am  I? # ANR-11-LABX-0071  and the  Université de Paris IdEx  # ANR-18-IDEX-0001  funded by the  French Government through its Investments for the Future program  (to D. D. ), and the  Human Frontier Science Program  ( RGP0038/2018 ) (to C.
Code availability The codes of MATLAB-based custom program for AFM analyses  136  and triple colocalization analyses  139  are available on GitHub (https://github. com/bryantdoss/ matlab-afm-indentation, https://doi. org/10. 5281/zenodo. 4543281 and https://github. com/simondebeco/Colocalization-Analyzer, https://doi. org/10. 5281/zenodo. 4501944).
Participants We included 17 healthy volunteers (7 men and 10 women: Mean age = 50. 4 years, SD = 14. 9, range 23-70). They were all right handed on their declaration. All participants gave their written informed consent to take part in this study, which was approved by the Poitiers-France III West institutional review board (no. 05/12/16; AVCPOSTIM study-clinical trial identifier NCT01677091). Exclusion criteria were (1) contraindication to magnetic field exposure, (2) neurological disease, and (3) history of balance disorders. All participants had normal or corrected-to-normal vision.
The instruction was "Press the button when the bar is aligned with a virtual plane dividing your corporeal space into two equal parts" (Figure  2  and Supplementary File S2). During the control condition, the same sequence of bar movements was presented, but participants were instructed to press the button when the bar changed direction. Each fMRI sequence lasted 3 min and followed a block design: 30 s blocks of task and 30 s block of control conditions. In each block, the bar passed through the center six times at a constant speed of two degrees per second  [20]. The process is similar for both tasks. The tasks were implemented using E-Prime 2. 0 Professional software (Psychology Software Tools, Pittsburgh, PA, USA) and presented using goggles (NordicNeuroLab, Bergen, Norway) attached to the head coil to immerse participants in the task without any spatial clues.
MRI Acquisition MRI acquisition was performed on a 3 tesla scanner (Magnetom Verio; Siemens Medical Solutions, Erlangen, Germany) with a 12-channel head coil. The alignment of head, shoulders, trunk, and limbs was checked by the experimenter before MRI acquisition. The head and the trunk were maintained with foam pads. During the control condition, the same sequence of bar movements was presented, but participants were instructed to press the button when the bar changed direction. Each fMRI sequence lasted 3 min and followed a block design: 30 s blocks of task and 30 s block of control conditions. In each block, the bar passed through the center six times at a constant speed of two degrees per second  [20]. The process is similar for both tasks. The tasks were implemented using E-Prime 2. 0 Professional software (Psychology Software Tools, Pittsburgh, PA, USA) and presented using goggles (NordicNeuroLab, Bergen, Norway) attached to the head coil to immerse participants in the task without any spatial clues.
Interleaved slices were acquired parallel to the anterior commissure posterior commissure line with no gap. In total, the experiment was completed in 15 min. Images were preprocessed and analyzed using Statistical Parametric Mapping (SPM12; Wellcome Department of Imaging Neuroscience, University College London, London, UK  [22] ; revision 7487) implemented in MATLAB. Morphological images were segmented, corrected for bias, and normalized to Montreal Neurological Institute atlas space (MNI). Slice timing, spatial realignment, distortion correction, coregistration to the morphological images, and normalization were applied, followed by 6 mm full width at half maximum (FWHM) isotropic Gaussian kernel smoothing. The canonical hemodynamic response function and its temporal derivative were used for model estimation, and three contrasts were evaluated: "SSA > control condition", "SLB > control condition", and "SLB > SSA".
Acknowledgments: I would like to thank  Assia Jaillard  for providing insightful feedback and  Cathy Stinear  for her guidance and support. MRI data acquisitions were carried out in the Neurinfo MRI research facility of the  University of Rennes I. Neurinfo  is funded by the  European Union (FEDER) ,  French Government, Brittany regional Council ,  Rennes Metropole ,  INRIA ,  INSERM , and  Rennes University Hospital.
While WT cells were able to utilize BSA, Phafin2 -/cells were unable to scavenge extracellular BSA and showed reduced proliferation (Fig. 8c ). These results indicate that Phafin2 is required for functional macropinocytosis in cancer cells. Screening the publicly available cBioportal cancer genome database  38, 39  for cancers with alterations in Phafin2 showed that Phafin2 is frequently amplified across multiple cancers, whereas deletions are very rare (Fig. 8d, e ). This raises the possibility that moderate overexpression of Phafin2 is advantageous for cancer cells, potentially by supporting improved nutrient scavenging.
Antibodies. The following antibodies were used: Phafin2 antibody, Sigma-Aldrich (HPA024829) (WB, 1:500). FilaminA antibody, Abcam (ab76289) (WB, 1:1000). Atto-488 conjugated anti-GFP nanobody, Chromotek (gba488) (IF, 1:100). γ-Tubulin antibody, Sigma-Aldrich (T5326)(WB, 1:10000). aTubulin antibody, Sigma-Aldrich (T5168) (WB, 1:20000). Rabankyrin-5 antibody (Abnova, H0005 1479-B01P) (IF, 1:100). MBP antiserum (New England Biolabs, E3080s) (WB,1:10000). Donkey anti-mouse Alexa555 (Molecular Probes. A31570)(1:500). Plasmids. pEYFP-Rabankyrin-5 was a gift from Marino Zerial;  10  EYFP was exchanged with mCherry. The following plasmids were obtained from Addgene: Myo1E-pmAppleC1 was a gift from Christien Merrifield (Addgene #27698 30 ,), pH2B-mCherrry-IRES-Neo was a gift from Daniel Gerlich (Addgene #21044  53  ), pDisplay-Phuji was a gift from Robert Campbell (Addgene #61556 29 ,), pEGFP-AKT-PH was a gift from Tobias Meyer (Addgene #21218 54 ); EGFP was replaced with mCherry.
To enhance the rate of macropinosome formation, cells were stimulated by adding HGF (50 ng/ml) directly before imaging. For imaging of Lifeact-SNAP, cells were stained with SiR647-SNAP (New England Biolabs) according to the manufacturer's protocol. Images were deconvolved using softWoRx software and processed in Fiji  59, 60. TIRF microscopy. TIRF microscopy was performed with the Ring-TIRF mode of the OMX V4 system described above, using a 60 × 1. 49 NA objective (Olympus), using 488 nm and 561 nm laser lines. All other imaging conditions were as described above. High-resolution light-sheet microscopy. Light-sheet microscopy was performed using a custom-built oblique plane microscope. The microscope layout followed the general plan for a stage scanning OPM microscope described in Sapoznik et al.
The excitation beam was coupled into the light path by a dichroic mirror placed between TL2 and the remote objective. We used a gaussian light sheet generated by an ASI light sheet generator (ASI Imaging) equipped with a cylindrical lens, coupled to a Toptica laser source (Toptica) with 405, 488, 561, and 647 nm laser lines. Image acquisition was performed by stage scanning. All hardware was synchronized by an ASI Tiger controller (ASI Imaging) and controlled by the ASI diSPIM plugin in MicroManager  62. Deconvolution and deskewing were performed using the LLSPy software package (https://github. com/ tlambert03/LLSpy/), the resulting images were visualized using Imaris. Structured illumination microscopy. Three dimensional SIM imaging was performed on Deltavision OMX V4 microscope with an Olympus x60 NA 1.
42 objective and three PCO. edge sCMOS cameras and 488 nm and 568 nm laser lines. Cells were illuminated with a grid pattern, and for each image plane, 15 raw images (five phases and three rotations) were acquired. Super-resolution images were reconstructed from the raw image files, aligned and projected using Softworx software (Applied Precision, GE Healthcare). Images were processed in ImageJ/Fiji  60. Release of mitochondrially tethered MTM1 using the RCD1 reversible dimerization system. RPE1 cells stably expressing Phafin2-GFP were transfected with a Tom20 mitochondrial targeting signal (MTS)-mTagBFP2-3xSNAPtag construct and an mCherry-FKBP-MTM1 (or MTM1 C375S) construct and incubated overnight in media containing 1 μM rCD1 (Sirius Fine Chemicals). rCD1 caused heterodimerization of the FKBP and SNAPtag moieties, anchoring MTM1 to the mitochondria.
For time-lapse experiments, fields of view were selected at the microscope in a 37 °C chamber. Then, the bacteria were added to the cells before the beginning of the acquisition. Time-lapse experiments were performed on an inverted Deltavision epifluorescence microscope (GE Healthcare) and a 60x oil objective. Images were deconvolved using softWoRx software Immunostaining and stimulation of macropinocytosis. siRNA treated cells grown on glass coverslips were stimulated with HGF (50 ng/ml) for 15 minutes in full medium (DMEM-F12, 10% FCS, Pen/Strep), fixed with 3% formaldehyde (Polysciences, 18814) for 15 min on ice, and permeabilized with 0. 05% saponin (Sigma-Aldrich, S7900) in PBS. Fixed cells were then stained with a mouse anti-Rabankyrin-5 antibody (Abnova, cat no: H0005 1479-B01P) at room temperature for 1 h, washed in PBS/saponin, stained with a fluorescently labeled secondary antibody (donkey anti-mouse Alexa555, Molecular Probes CatNo.
: A31570) for 1 h, washed in PBS and mounted with Mowiol (Sigma-Aldrich) containing 2 μg/ml Hoechst 33342 (Thermo Fisher Scientific, H3570). Alexa488-Phalloidin (cat no: A12379) or Alexa647-Phalloidin (cat no: A22287) were used to detect filamentous actin. High content imaging and analysis of Rabankyrin-5 dots and actin ruffling cells. Widefield images of siRNA transfected cells labeled for endogenous Rabankyrin-5 or filamentous actin (Alexa488-Phalloidin) were acquired automatically by an Olympus ScanR high content microscopy system using a UPLSAPO 40x objective and analyzed automatically by the ScanR software. After background subtraction, dots of Rabankyrin-5 > 30 pixels were segmented automatically by the ScanR software based on an intensity threshold, and the number of dots per cell was measured.
The total number of cells was quantified automatically by the detection of Hoechst nuclear stain. Identical settings were used for all conditions within an experiment. Cells with prominent actin ruffling were scored manually. Confocal fluorescence microscopy. Confocal images were obtained using LSM710 confocal microscope (Carl Zeiss) equipped with an Ar-laser multiline (458/488/514 nm), a DPSS-561 10 (561 nm), a laser diode 405-30 CW (405 nm), and a HeNe laser (633 nm). The objective used was a Plan-Apochromat 63 × /1. 40 oil DIC III (Carl Zeiss). Image processing was performed with basic software ZEN 2010 (Carl Zeiss) and Fiji software. Measurement of dextran and DQ-BSA uptake by flow cytometry. Fluid-phase uptake was assayed by measuring fluorescent dextran uptake using flow cytometry. Fluorescent dextran Alexa Fluor 488 10 kDa was added to cells (0, 5 mg/ml) stimulated with HGF and cells were incubated for 20 min at 37°C.
Cell outlines were segmented in an ImageJ script using the plasma membrane marker as a guide. Background values (compensation for residual nonspecific dextran and imperfect deconvolution) were obtained from an ROI in each image, excluding all cells segmented. Where the ImageJ script failed to generate a good quality segmentation, we manually generated the ROIs and took background values from a 100 × 100 pixel square outside cells. The intracellular dextran fluorescence was computed by subtracting the background from mean fluorescence within the cell and multiplying by cell area to obtain the integrated intensity of the cell. Measurement scripts are available at https://github. com/koschink/Phafin2. Measurement of cell proliferation under amino acid limiting conditions. MIA-PACA2 cells, either WT or Phafin2 -/cells, were seeded in 6-well plates (50,000 cells/well).