dataesr/scientific-paragraphs-categorization huggingface dataset:
Peptides were separated using a linear gradient (92. 5% A to 84% A) for 26 min, followed by a second one (84% A to 75% A) for 14 min and a wash at 60% B for 2 min. The flow rate was 1. 8 μL/min. Protein identification was accomplished using data acquired by LC-MS/MS. The MS/ MS spectra were matched to the corresponding peptide sequences found in the UniProt human protein database using Mascot (Matrix Science, version 2. 2. 06. ) software.
Multiplexed multiple reaction monitoring (MRM) assay For each of the selected proteins, five MRM-suitable peptides were selected by CellCarta's in-house MRM Peptide Selection software. If possible, peptides that were detected by mass spectrometry were prioritized. The selected peptides were synthesized by JPT Peptide Technologies (Germany). Synthesized peptides were resolubilized in 25%/75% water/DMSO (v/v), pooled and diluted with 0. 2% formic acid in water to a concentration of 200 pmol/mL. This peptide mix was used to develop the MRM assay. The optimal 2 transitions (combination of peptide precursor and fragment ion mass-to-charge ratio that are monitored by the mass spectrometer) per peptide were determined using selected reaction monitoring (SRM)-triggered MS/MS on a QTRAP 5500 instrument (AB Sciex) coupled to a nanoAcquity UPLC (Waters).
Eight μL of material (~10 μg) was analyzed by LC/MRM-MS. Peptide separation was achieved using a BioBasic C18 column (Thermo) (320 μm × 150 mm, 5 μm particle size). The mobile phases were (A) 0. 2% formic acid in water and (B) 0. 2% formic acid in acetonitrile. Peptides were separated using a linear gradient (92. 5% A to 60% A) for 21 min, followed by a wash at 60% B for 2 min. The flow rate was 10 μL/ min. The transition peak areas were integrated using Elucidator software (Rosetta Biosciences) in combination with software developed at Cell-Carta for automated MRM peak integration.
Transcriptomic analyses Gene expression data were retrieved from the GSE19422 dataset (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE19422)  [23]  which includes 6 normal adrenal medulla and 61 Pheos. Consistently with the proteomic analysis, the 23 paraganglioma samples were not considered. The GSM483021 Pheo sample was excluded from our analysis because its transcriptomic profile, as revealed by clustering and UMAP analyses (data not shown), was similar to that of normal samples, suggesting that it was misidentified. The expression level of each gene was calculated as the geometric mean of the significantly differentially expressed probes identified with the GEO2R web tool using a Benjamin-Hochberg FDR correction (q-values < 0. 05). Genes of the regulated exocytosis (#BPGO:0045055), secretory granule (#CCGO:0030141), catecholamine biosynthetic process (#GO:0042423), monoamine transport (#GO:0008504) and dopamine catabolic process (#GO:0042420) GO terms were then selected for further analysis.
Unsupervised hierarchical clustering was performed with Bioconductor (v. 3. 13) and R software (v. 4. 1. 0) using Pearson correlation as distance calculation and complete linkage. The Fold Change (FC) for each gene was calculated from the median expression values for the normal and Pheo groups and only those with a FC greater than or equal to 2 were retained for further analysis. The correlation between the expression level of common genes and proteins was evaluated in both cytosolic and membrane fractions using Spearman correlation (GraphPad Prism v. 8. 1). In order to highlight functional clusters, an integrative analysis was conducted with Cytoscape software (v. 3. 8. 2,  [24] ) using expression data and protein interaction data retrieved from STRING-DB (https://string-db. org, [25] ). An edge-weighted force directed layout has been applied to the interaction network.
Differential protein expression of the exocytic machinery in human pheochromocytomas Since catecholamine release is altered in Pheos, we asked whether proteins of the core machinery of secretory granule exocytic process could be deregulated. To do so, we performed a mass spectrometry analysis comparing 5 pairs consisting of human Pheos with their respective matched adjacent non-tumor tissues. To increase detection and sensitivity, we conducted the proteomic analysis on purified subcellular fractions in place of total homogenate. Two main subcellular fractions were isolated from each tissue sample, a membrane fraction enriched for organelles and vesicles derived from the exocytic pathway and a fraction enriched in cytosolic proteins. From our proteomic dataset, we selected proteins from the regulated exocytosis (#BPGO:0045055) and secretory granule (#CCGO:0030141) GO terms.
The volcano plots in Fig. 5B  show the relationship between the pvalues and the fold change in expression for proteins detected in the cytosol or in membranes-enriched fractions. The unsupervised hierarchical clustering of all differentially expressed proteins in row according to the paired samples in columns, is presented in Fig. 5C  for the corresponding cellular compartment. Note that the expression of proteins found in common in the membranous and the cytosolic fractions varies in the same direction (Supplementary Fig. 1 ). This specific dataset of proteins corresponding to the proteins involved in the machinery of secretory granule exocytotic process and in catecholamine metabolism clearly differentiates, by their modulation of expressions, the Pheo samples from the non-tumor samples.
The functions of the deregulated proteins linked to secretory granule exocytosis include mainly: secretory granule organization and biogenesis, hormone processing and catecholamine metabolism, vesicular trafficking, docking, priming, membrane fusion, actin cytoskeleton organization, and small GTPases (Supplementary Table  2  and Fig. 5D ). Hence, we observed that numerous secretory granule cargos, enzymes involved in the catecholamine metabolism as well as different proteins involved in the control of the frequency and the dynamic of the exocytic events are up-regulated. To further assess the relevance of our data, we have compared our proteome dataset to a published gene expression analysis of 60 Pheos and 6 normal adrenal medulla tissues (  [23]  https://www. ncbi. nlm. nih. gov/geo/query/acc.
cgi?acc=GSE19422). Using this analysis, we have extracted 183 genes that belong at least to one of the 5 GO terms used in the proteomic analysis and that are significantly differentially expressed by at least two-fold between Pheos and normal adrenal tissues. Interestingly, among these 183 genes, 64 are common to our proteome dataset. As observed at the level of the protein expression changes, the hierarchical clustering of these 64 differentially expressed genes efficiently separated the tumor samples from the normal adrenal tissues (Fig. 6A ). Moreover, the expression of 58 of these 64 genes (91%) shows the same variation trend as the protein expression, both in the cytosol and in the membranes (Fig. 6B  and C  and Supplementary Fig. 2 ). To visualize whether the deregulated proteins identified in this comparison were highly connected to each other, we performed a protein-protein interaction (PPI) network analysis using Cytoscape (  [24] , Fig.
7 ). Interestingly, one major significant cluster is composed of proteins that are overexpressed both at the protein and at the gene levels (blue squares). The cluster includes proteins involved in the secretory granule composition such as various chromogranins (CHGA, CHGB, SCG2, SCG3, SCG5), in catecholamine metabolism (TH, DDC, DBH), monoamine vesicular transport (VMAT/SLC18A-1 and -2) or hormone processing (PCSK1, PCSK2, CPE, PAM), in granule docking and fusion such as different SNAREs or SNAREs interacting proteins (SNAP25, STX1A, STXBP1, SYT1) and Rab GTPases (RAB3A, RAB3D, RAB27B). Finally, to further validate our dataset, we analyzed the global expression change of few key overexpressed proteins (17) selected from our proteomic dataset. To do so, we performed a multiplexed MRM-MS assay in total protein homogenates prepared from another independent cohort of 25 pairs of human Pheos and their matched non-tumor tissues  [26].
Funding and Acknowledgements This work was financially supported by  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the Cancer Plan to SG and LB (Single Cell 2018 N •  19CS004-00 ); by the  University of Strasbourg Institute for Advanced Study (USIAS)  for a Fellowship, within the  French national programme  " Investment for the future" (IdEx-Unistra ) to SG; by the  Conseil Régional de Normandie  to  C. Dubessy ; by grants from the  Agence Nationale pour la Recherche ("SecretoNET", N •   ANR-16-CE17-0022-01 ) and from the  Ligue contre le Cancer  to SG (CCIR Grand-Est) and to  C. Dubessy  ( Comité Normand ); by.
TrackMate's new detectors can also be used to perform 3D segmentation. Indeed, by swapping the Z dimension of the source image with time, TrackMate can link 2D segmentation results across Z planes and generate a 3D segmentation result. This new feature makes the segmentation of 3D objects accessible, flexible, and possible without programming knowledge (Figure  2f , Supplementary Figure  4 , and Movie 9-11). TrackMate v7 currently offers a choice of 10 segmentation detectors (plus the integration of custom models for some of them) and five particle-linking algorithms for tracking the detected objects. To facilitate choosing an optimal combination for a specific dataset, we developed an additional module, the TrackMate helper (Supplementary manual and Supplementary Figure  5 ). This module is a user-friendly application that performs parameter sweeps over any combination of detectors and particle-linking algorithms.
Using the ground-truth provided by the user, TrackMate helper computes the Cell-Tracking-Challenge (CTC) metrics  19  for each parameter combination and reports the optimal one for each of the CTC metrics (Table  S2 ). In a nutshell, TrackMate helper allows the optimization of the tracking parameters for a whole dataset systematically. Altogether, TrackMate now enables powerful segmentation approaches for tracking purposes directly in Fiji within a user interface already familiar to many. We envision that by enabling scientists to resolve complex tracking problems more efficiently, this new version of TrackMate will accelerate discoveries in Life Sciences. We expect that TrackMate will continue to evolve in the years to come. In particular, as core libraries handling 3D objects are further developed in Fiji, the analysis of 3D object contours and shapes during tracking could become an invaluable addition.
TrackMate was also built as a software platform to be extended by others independently and is documented as such. As DL-based segmentation and tracking algorithms are being developed, we hope contributors will consider TrackMate as a platform to accelerate the dissemination of their work towards researchers in the Life Sciences and beyond  18, 19. c. MDA-MB-231 cells stably expressing an ERK activity reporter (ERK-KTR-Clover) and labeled using SiR-DNA were recorded live using a wide-field fluorescence microscope over 17 hours. Cell nuclei were automatically tracked over time using a StarDist model available in TrackMate (see Movie 3). For each tracked cell, the average intensity of the ERK reporter was measured in their nucleus over time (directly in TrackMate). Changes in ERK activity and in instant velocity are displayed as heatmaps (blue high, yellow low).
Tracking migrating breast cancer cells Migrating MCF10DCIS. com cells were tracked using either StarDist directly implemented within TrackMate or using Cellpose and then TrackMate. To track MCF10DCIS. com cells labeled with sir-DNA using StarDist and TrackMate, a custom StarDist model was generated using the ZeroCostDL4Mic platform  3, 4. This custom StarDist model was trained for 100 epochs on 72 paired image patches (image dimensions: 1024x1024, patch size: 1024x1024) with a batch size of 2 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v1. 12. 2). The StarDist "Versatile fluorescent nuclei" model was used as a training starting point. Key python packages used include TensorFlow (v1. 15), Keras (v2. 3. 1), CSBdeep (v0. 6. 1), NumPy (v1. 19. 5) and Cuda (v10. 1. 243).
The training was accelerated using a Tesla P100 GPU. This model generated excellent segmentation results on our test dataset (average F1 score > 0. 96). This model, the training dataset as well as the code used for training is available on Zenodo  5. In TrackMate, the StarDist detector custom model (score threshold = 0. 41 and overlap threshold =0. 5) and the LAP tracker (linking max distance = 30 µm; track segment splitting = 15 µm) were used. Tracks were filtered in the function of their total distance traveled and tracks shorter than 80 µm were excluded. To track MCF10DCIS. com cells expressing lifeact-RFP (cell line described here  6  ) and labeled with sir-DNA, cells were first segmented using the ZeroCostDL4Mic cellpose 2D notebook (v1. 12,  3, 7  ). The cellpose model Cyto was used for the segmentation and the lifeact staining was used as the main segmentation channel.
The Sir-DNA channel was used as the secondary segmentation channel. The following cellpose parameters were used Flow threshold = 0. 4 and Cell probability threshold = 0, Object diameter: 50. This approach generated excellent segmentation results on our test dataset (average F1 score > 0. 93). In TrackMate, the label image detector and the LAP tracker linking max distance = 30 µm; track segment gap closing = 15 µm and 2 frames; track segment splitting = 15 µm) were used. Tracks were filtered in the function of the total number of spots detected and tracks with less than 40 spots were excluded. This dataset is available on Zenodo  8, 9.
Tracking migrating T cells. T cells migrating on ICAM-1 were automatically tracked using StarDist directly implemented within TrackMate. The StarDist model used was described previously  10  and is publicly available on Zenodo  11, 12. This model generated excellent segmentation results on our test dataset (F1 score > 0. 99). In TrackMate, the StarDist detector custom model (Score threshold = 0. 41 and Overlap threshold = 0. 5) and the Simple LAP tracker (linking max distance = 30 µm; gap closing max distance = 15 µm, gap closing max frame gap = 2 frames) were used. This dataset is available on Zenodo  11.
3) every 5 minutes for 3 hours. Cell nuclei were automatically tracked over time by using StarDist in TrackMate. To track the nuclei of U2OS cells, a custom StarDist model was trained using the ZeroCostDL4Mic platform  3. The training source for the model was generated from 25 manually annotated images (Dimensions: 2048x2048) using the LOCI plugin in Fiji. The generated training source and target were randomly cropped into size 1024x1024, rotated, flipped, and multiplied by five using the Augmentor ZeroCostDL4Mic notebook  3, 13  to generate a dataset of 120 paired images. The custom StarDist model was trained for 200 epochs on the 120 paired image patches (image dimensions: 1024x1024, patch size: 1024x1024) with a batch size of 2 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v1.
12. 2)  3  ). Key python packages used include TensorFlow (v1. 15), Keras (v2. 3. 1), CSBdeep (v0. 6. 1), NumPy (v1. 19. 5) and Cuda (v11). The training was accelerated using a Tesla P100GPU. This model generated excellent segmentation results on our test dataset (average F1 score > 0. 918). In TrackMate, the StarDist detector custom model (Score threshold = 0. 41 and Overlap threshold = 0. 5) and the LAP tracker (linking max distance = 20 µm; Track segment gap closing = 25 µm, Gap closing max frame gap = 10 frames) were used. Tracks were filtered in function of their track duration and tracks shorter than 34 frames (2h 40min) were excluded. This dataset is available on Zenodo. To track the nuclei of MDA-MB-231 cells, the "Versatile fluorescent nuclei" StarDist model was used. In TrackMate, the StarDist detector (Score threshold = 0.
41 and Overlap threshold = 0. 5) and the LAP tracker (linking max distance = 40 µm; Track segment splitting = 30 µm) were used. Tracks were filtered in function of their duration and only the tracks spanning the whole movie were considered for further analysis (directly in TrackMate). For each tracked cell, the average intensity of the ERK reporter was measured in their nucleus over time (directly in TrackMate). To visualize the changes in ERK activity over time, results were uploaded to PlotTwist 14 , data were rescaled between 0 and 1, and visualized as heatmaps. This dataset is available on Zenodo  15.
Tracking Mouse hematopoietic stem cells migrating in hydrogel microwells. Mouse hematopoietic stem cells migrating in a hydrogel microwell  16  were automatically segmented using cellpose (Cyto model) implemented in the ZeroCostDL4Mic platform  3, 7. The following Cellpose parameters were used: flow threshold = 0. 4 and cell probability threshold = 0, object diameter = 17. This approach generated excellent segmentation results on our test dataset (average F1 score > 0. 93). The resulting label images were automatically tracked using TrackMate. In TrackMate, the label image detector and the LAP tracker (linking max distance = 30 µm; track segment gap closing = 15 µm and 2 frames; track segment splitting = 15 µm) were used. Spots were filtered in function of their circularity and area. Tracks were filtered in function of the total distance traveled; tracks shorter than 80 µm were excluded.
Tracking glioblastoma-astrocytoma cells migrating on a polyacrylamide gel Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel were automatically segmented using a custom cellpose trained using the ZeroCostDL4Mic platform  3, 7. This cellpose model was trained for 500 epochs on 214 paired image patches (image dimensions: 520x 696), with a batch size of 8, using the cellpose ZeroCostDL4Mic notebook (v 1. 13). The cellpose Cyto2 model was used as a training starting point. The training was accelerated using a Tesla K80 GPU. The following cellpose parameters were used for inference: flow threshold = 0. 4 and cell probability threshold = 0, object diameter = 100. This approach generated excellent segmentation results on our test dataset (average F1 score > 0. 97). The resulting label images were automatically tracked using TrackMate.
Once the bacteria-containing droplet had dried up, the agar pad was flipped down onto a Fluorodish (Ibidi, 60~µm-Dish, 35~mm high Glass bottom). Fluorescently labeled proliferating bacteria were acquired using an inverted spinning-disk confocal microscope (Nikon, TI Eclipse) equipped with a 100X immersion objective (PlanFluor, NA = 0. 5 -1. 3) at 37°C in the presence of 5\% CO 2. Bacterial fluorescence was imaged in time-lapse at a 5 min frame rate with an exposure time of 300 ms for 5. 5 hours and recorded with a CMOS Camera (Photometrics, 95BPrime) using Metamorph Imaging Software (Molecular Devices). The focus was maintained with the Perfect Focus System (PFS, Nikon). We trained an ilastik model using the Pixel Classification workflow using images from these experiments. This model was then used in the TrackMate-Ilastik detector, with a threshold on the probability map of 0.
5. Spurious detections larger than 4000 µm 2 were removed prior to linking. We used the LAP tracker for linking, using a max linking distance of 1 µm, with a max gap-closing distance of 2 µm over a maximum of 2 frames, and detecting cell divisions over a maximal distance of 2 µm. We then filtered out tracks that start after 43 minutes. This dataset is available on Zenodo  24.
) equip\-ped with a 100x (NA 1. 4) oil, Plan-Apochromat, M27 (Zeiss). To generate 3D labels, nuclei were detected and tracked across the Z volume using StarDist implemented in TrackMate. In TrackMate, the StarDist detector (score threshold = 0. 41 and overlap threshold = 0. 5) and the LAP tracker (linking max distance = 1 µm, track merging and splitting enabled) were used. Detected spots were filtered in function of their mean intensity to exclude spots with weak intensities. Tracks were filtered in function of the number of spots per track and only the tracks with more than 3 spots were considered for further analysis (directly in TrackMate). In TrackMate, tracked nuclei were then exported as a label image to create 3D labels. 3D labels were then visualized using the FPBioimage software  27.
The video was also generated using the FPBioimage software. This dataset is available on Zenodo  28. Confocal images of Arabidopsis Thaliana floral meristem  29  and light-sheet images of a developing Drosophila Melanogaster embryo  30, 31  were automatically segmented using Cellpose (Cyto2 model) implemented in the ZeroCostDL4Mic platform  3, 7. The following Cellpose parameters were used: flow threshold = 0. 4 and cell probability threshold = 0, object diameter = 0. This approach generated excellent segmentation results on our test datasets (Arabidopsis Thaliana floral meristem, average F1 score > 0. 97; Drosophila Melanogaster embryo, average F1 score > 0. 89). To generate 3D labels, the 2D label images were tracked using TrackMate. In TrackMate, the label image detector and the simple LAP tracker were used.
Migrating cancer cells imaged using fluorescence microscopy to visualize their nuclei In this dataset, cells were labeled to visualize their nuclei using fluorescence microscopy. Nuclei can be segmented using several Deep-Learning (DL) based algorithms such as StarDist and cellpose that both ship a built-in model capable of segmenting nuclei. We also trained a custom StarDist model using other movies of the same dataset to test whether it would improve our tracking results. Since nuclei are more-or-less round and convex objects, they can be detected with the Laplacian-of-Gaussian (LoG) detector or the Difference-of-Gaussian (DoG) detector. Therefore we tested the performance of these five detectors to track this dataset. For the linking step, we used the Linear-Assignment-Problem (LAP) framework (first introduced in  32  ) with or without cell-division detection, the Kalman filter tracker, and the nearest-neighbor tracker.
We note that our custom StarDist model scores the best for this metric, reflecting its better robustness against false positive and false negative detections. The DET and the TF metrics also privilege this detector. We find that the tracker choice has only a small impact on these three metrics. Even the nearest neighbor tracker performs relatively well here. This result is likely due to the relatively simple motility displayed by these cells. Indeed, the nuclei move in a coordinated manner, and they do not cross and seldom touch another. These results illustrate that the detection step is crucial for such tracking problems. The CT scores are comparatively lower, which was also noted in  30  for other trackers and datasets. The CT metric is very sensitive to the slightest inaccuracy in object linking and in detecting cells and their division.
The best CT score is obtained when our StarDist custom model is combined with the LAP linker that enables cell division. Using our StarDist custom model with the LAP linker without cell division detection lowers the CT score by 20%. The BC score is non-zero only when using the LAP tracker with cell division. The CT score is also strongly influenced by the choice of the detector. This dataset illustrates the importance of choosing an adequate detector to obtain high tracking accuracy, even when analyzing high-quality images acquired at a high frame rate. Modern segmentation algorithms based on DL perform better than classical detectors, even with objects as simple as nuclei. However, their use comes at the cost of execution time. Indeed, the TIM metric goes from about 2 seconds when using the LoG detector to more than 2 minutes with our custom StarDist detector.